Real-time Investigation of Underlying Physiology behind ...

Fakultät für Medizin

Nuklearmedizinische Klinik und Poliklinik

Real-time Investigation of Underlying

Physiology behind PET Tracer Uptake based

on Positron Imaging of a Microfluidic Chip

and a Window Chamber

Zhen Liu

Vollständiger Abdruck der von der Fakultät für Medizin der Technischen Universität München zur

Erlangung des akademischen Grades eines

Doctor of Philosophy (Ph.D.)

genehmigten Dissertation.

Vorsitzender: Prof. Dr. Claus Zimmer

Betreuerin: Prof. Dr. Sibylle Ziegler

Prüfer der Dissertation:

1. Prof. Dr. Gabriele Multhoff

2. Prof. Dr. Gil Westmeyer

Die Dissertation wurde am 07.06.2016 bei der Fakultät für Medizin der Technischen Universität

München eingereicht und durch die Fakultät für Medizin am 17.08.2016 angenommen.

Real-time Investigation of the Underlying Physiology behind

PET Tracer Uptake based on Positron Imaging of a

Microfluidic Chip and a Window Chamber

Zhen Liu

München 2016

Abstract

Tumor metabolism and tumor microenvironment are two frontiers of current tumor research.

This thesis developed two imaging systems, a continuously infused microfluidic radioassay

(CIMR) system and a multimodal intravital molecular imaging (MIMI) system, to imaging

tumor cellular metabolism in vitro and tumor microenvironment in vivo, respectively. Both

imaging systems provide high-quality spatiotemporal measurements of radioactive tracers for

positron emission tomography (PET), which is achieved using a positron camera based on a

single-particle counting silicon pixel detector Timepix with high sensitivity and spatial

resolution.

Measuring the cellular pharmacokinetics is difficult. Conventional uptake measurements or

microfluidic radioassay systems need to load, unload and purge tracer from the cell culture

during the measurements and it is difficult to provide dynamic information. The CIMR system

provides a method for continuous in-culture measurement of cellular uptake by simultaneous

imaging of a medium chamber and a cell chamber. Medium diluted with a radioactive tracer

flows through the medium chamber and cell chamber continuously at a constant low speed.

Positrons emitted from the cells and from the tracer in the medium are measured using the

positron camera. To test the feasibility of the CIMR system, the influence of glucose

concentration on 18F-FDG uptake kinetics was investigated. Human tumor cell lines SkBr3 and

Capan-1 were incubated with media of 3 different glucose concentrations and then measured

with 18F-FDG on the CIMR system. The relative uptake ratios obtained from CIMR

measurements correlated with those from the conventional uptake experiments. The relative

standard deviations of relative uptake ratios of CIMR are substantially lower than the

conventional uptake experiments. A cellular two compartment model was applied to estimate

the cellular pharmacokinetics on CIMR data. The estimated pharmacokinetic parameters were

compared to the expressions of glucose transporter-1 (GLUT1) and hexokinase-II (HK2)

measured by quantitative real-time polymerase chain reaction (qPCR). For SkBr3, the estimated

pharmacokinetic parameters can be fitted with the mRNA expressions using a fixed Km with the

determinant coefficient (R2=0.73 for 𝑘1and R2=0.97 for 𝑘3). However, for Capan-1, it is not

possible to fit the estimated cellular kinetics with the corresponding mRNA levels using a fixed

Km.

Tumor microenvironment is reflected by several physiological features such as microvasculature,

pH, oxygen partial pressure (pO2), and metabolism. Multimodal molecular imaging with PET

and MRI provides advanced in vivo methods to capture multiple physiological properties of the

tumor microenvironment. However, the interpretation of multimodal imaging to underlying

tumor microenvironment is challenging due to the discrepancies between macroscopic and

microscopic images of multimodal imaging, as well as in vivo and in vitro images. The proposed

MIMI system provides an advanced platform to bridge these discrepancies and allow multiple

in-depth investigations on the same intact tumor tissue. The MIMI system set up a dorsal skin

window chamber for a rat tumor model, which is compatible with multimodal imaging and

enables co-registration of images with different resolutions. High-resolution positron imaging,

magnetic resonance imaging (MRI), fluorescence imaging and luminescence sensor imaging

were included in this MIMI system. The fiducial markers of the window chamber were adapted

to the multi-imaging modalities and allowed precise physical co-registration of various images.

This system offered a tool for the regional investigation and longitudinal observation of

underlying physiology in intact tumor tissue.

Zusammenfassung

Der Tumormetabolismus und die Tumormikroumgebung stellen zwei neue Herausforderungen

der aktuellen onkologischen Forschung dar. Im Rahmen dieser Doktorarbeit wurden zwei

Bildgebungssysteme entwickelt, ein kontinuierlich perfundiertes microfluidisches System zur

Messung von Radioaktivität (CIMR) und ein multimodales intravitales Bildgebungssystem zur

molekularen Bildgebung (MIMI), um den zellulären Metabolismus in vitro und den

Tumormetabolismus in vivo darzustellen. Beide Bildgebungssysteme ermöglichen

hochqualitative räumlich-zeitlich aufgelöste Messungen radioaktiver Tracer für die

Positronenemissionstomographie (PET) durch eine Positronenkamera, welche auf einem

Silikonpartikeldetektor Timepix basiert, der einzelne Partikel mit hoher Sensitivität und

räumlicher Auflösung quantifiziert.

Die Messung von zellulärer Pharmakokinetik stellt sich schwierig dar. Konventionelle

Aufnahmemessungen bzw. mikrofluidische Radioaktivmessungen müssen beladen, entladen

und Tracer aus der Zellkultur entnommen werden. Dynamische Messungen stellen sich in

diesem Kontext indes als sehr herausfordernd dar. Das CIMR System ist eine Methode zur

kontinuierlichen Messungen der zellulären Aufnahme von Zellen in Kultur durch simultane

Bildgebung einer Kammer mit Zellkulturmedium und einer Kammer mit Zellen. Medium,

welches mit radioaktivem Tracer versetzt wurde, fließt durch die Kammer mit Zellkulturmedium

und die Kammer mit Zellen mit einer konstanten, niedrigen Geschwindigkeit. Positronen,

welche von den Zellen und von dem Tracer im Zellkulturmedium emittiert werden, wurden mit

der Positronenkamera gemessen. Um die Leistungsfähigkeit des CIMR Systems zu testen,

wurde der Einfluss der Glukosekonzentration auf die 18F-FDG-Kinetik untersucht. Die humanen

Tumorzelllinien SkBr3 und Capan-1 wurden mit Medium in drei unterschiedlichen

Glukosekonzentrationen inkubiert und anschließend mit 18F-FDG im CIMR System gemessen.

Das relative Aufnahmeverhältnis, welches aus den CIMR Messungen gewonnen wurde,

korrelierte wesentlich mit jenem, welches aus einem konventionellem Aufnahmeexperiment

erhoben wurde. Die relativen Standardabweichungen der relativen Aufnahmeverhältnisse der

CIMR Messungen sind wesentlich geringer als jene der konventionellen Aufnahmeversuche.

Ein zelluläres Zwei-Kompartiment-Modell wurde angewandt, um die zelluläre Pharmakokinetik

der CIMR Daten abzuschätzen. Die abgeschätzten pharmakokinetischen Parameter wurden mit

den Genexpressionsdaten von Glukosetransportern-1 (GLUT-1) und Hexokinase-II (HK2),

welche mittels quantitativer Echt-Zeit-Polymerasekettenreaktionsmessungen (qPCR) erhoben

wurden, verglichen. Die hochqualitativen dynamischen Messungen erlauben die Abschätzung

der zellulären Pharmakokinetik mit hoher Sensitivität.

Die Tumormikroumgebung zeichnet sich durch einige physiologische Besonderheiten, wie z.B.

Mikovaskularisierung, pH, Sauerstoffpartialdruck (pO2) und Metabolismus aus. Multimodale

Bildgebung mit PET und MRT stellen fortgeschrittene in vivo Methoden dar um mehrere

physiologische Eigenschaften der Tumormikroumgebung zu erfassen. Dennoch stellt sich die

Interpretation der multimodalen Bildgebung auf Grund der zugrundeliegenden

Tumormikroumgebung als Herausforderung dar, basierend auf den Unterschieden zwischen

makroskopischen und mikroskopischen Bildern der multimodalen Bildgebung, so wie auch bei

in vivo als auch in vitro Bildern. Das vorgestellt MIMI System ist eine fortgeschrittene Plattform,

welche es ermöglicht diese Diskrepanz zu überbrücken und multiple, detaillierte

Untersuchungen eines gleichen, intakten Tumors erlaubt. Das MIMI System besteht aus einer

dorsalen Hautfensterkammer, welche mit multimodaler Bildgebung kompatibel ist und die Ko-

Registration von Bildern mit unterschiedlicher Auflösung ermöglicht. Hoch-aufgelöste

Positronenbildgebung, Magnetresonanztomographie (MRI), Fluoreszenzbildgebung und

Luminiszenzbildgebung wurden in dieses MIMI System integriert. Die Orientierungspunkte der

Fensterkammer wurden den Multimodalen Bildgebungsmodalitäten angepasst und ließen eine

physikalische präzise Ko-Registrierung verschiedener Bilder zu. Dieses System stellt ein

Werkzeug zur regionalen Untersuchung und zur longitudinalen Beobachtung der

zugrundeliegenden Physiologie von intaktem Tumorgewebe dar.

Contents

1. Introduction ............................................................................................................................. 1

1.1. Overview of tumor biology .............................................................................................. 1

1.2. Molecular Imaging ........................................................................................................... 4

1.2.1. Overview of molecular imaging ................................................................................ 4

1.2.2. Positron imaging ........................................................................................................ 6

1.2.3. Intravital imaging ....................................................................................................... 7

1.3. Linking of molecular imaging and tumor biology ............................................................ 8

1.3.1. Kinetic modeling ...................................................................................................... 10

1.3.2. Microfluidic radioassay ........................................................................................... 12

1.3.3. Window chamber ..................................................................................................... 14

1.4. Goals of the study and overview of this thesis ............................................................... 16

1.4.1. Goals of the study .................................................................................................... 16

1.4.2. Overview of this thesis ............................................................................................. 17

2. Materials and Methods for Continuously Infused Microfluidic Radioassay System ............ 26

2.1. Microfluidics .................................................................................................................. 27

2.1.1. Continuously infused microfluidic radioassay (CIMR) system ............................... 27

2.1.2. Detection procedure ................................................................................................. 28

2.1.3. Image processing and normalization........................................................................ 29

2.1.4. Cellular pharmacokinetic modeling ......................................................................... 33

2.2. Calibration and connections ........................................................................................... 35

2.2.1. Depth-dependent sensitivity correction ................................................................... 35

2.2.2. Influence of sensitivity ............................................................................................. 36

2.3. Comparison with conventional uptake experiments ....................................................... 38

2.3.1. Relative comparison ................................................................................................. 38

2.3.2. Quantitative comparison .......................................................................................... 38

2.4. Modeling strategy with square-function infusion profiles ............................................. 39

2.5. qPCR ............................................................................................................................... 39

3. Results for Continuously Infused Microfluidic Radioassay System ..................................... 41

3.1. Reproducibility and stability .......................................................................................... 41

3.2. Illustration of model fitting ............................................................................................. 42

3.3. Modeling strategy with square-function infusion profiles ............................................. 43

3.4. Comparison with conventional uptake experiments ....................................................... 44

3.4.1. Relative comparison ................................................................................................. 44

3.4.2. Quantitative comparison .......................................................................................... 45

3.5. Correlation of kinetic parameters to qPCR results......................................................... 46

4. Discussion for Continuously Infused Microfluidic Radioassay System ............................... 50

5. Materials and Methods for Multimodal Intravital Molecular Imaging System .................... 56

5.1. Multimodal compatible dorsal skin window chamber basics ......................................... 56

5.2. Animal and tumor model ................................................................................................ 58

5.2.1. Window chamber implantation ................................................................................ 59

5.2.2. Tumor transplantation .............................................................................................. 61

5.2.3. In vitro histological staining .................................................................................... 61

5.3. MRI imaging ................................................................................................................... 62

5.4. Positron imaging of 18F-FDG ......................................................................................... 63

5.5. Luminance oxygen sensor imaging ................................................................................ 63

5.6. Fluorescence imaging ..................................................................................................... 64

5.7. Data processing ............................................................................................................... 65

6. Results for Multimodal Intravital Molecular Imaging System ............................................. 67

6.1. MRI imaging ................................................................................................................... 67

6.2. Positron imaging ............................................................................................................. 68



6.5. In vitro histological staining ........................................................................................... 72

6.6. Integrated tumor microenvironment imaging ................................................................. 73

7. Discussion for Multimodal Intravital Molecular Imaging System ........................................ 74

7.1. Implantation with multimodal compatible window chamber ......................................... 74

7.2. Animal tumor model ....................................................................................................... 75

7.3. MRI imaging ................................................................................................................... 75

7.4. Positron imaging ............................................................................................................. 75



7.7. Multi-modality imaging .................................................................................................. 78

8. Summary ............................................................................................................................... 81

List of Abbreviations ................................................................................................................. 84

Acknowledgements ................................................................................................................... 88

Publications and Conferences ................................................................................................... 91

Introduction

1

1. Introduction

1.1. Overview of tumor biology

Tumor originally meant any form of swelling, neoplastic or not. Current terminology, however,

both medical and non-medical, uses tumor as a synonym of neoplasm [1]. Tumor is an abnormal

growth of tissue, and can be described as clonally derived (although not exclusively) growing

masses of excessively replicating cells with no real mechanism of regulation [2]. Generally, a

tumor can be sorted as a benign neoplasia or a malignant neoplasia. The latter refers to cancer,

which is characterized by uncontrolled cell division and the ability of these cells to invade other

tissues, either by a direct growth into adjacent tissues (invasion) or by a migration of cells to

distant tissues (metastasis) [3]. Tumor progression can be defined with grading, denoting

increased lack of differentiation of the tumor cells from grade I to grade IV. The tumor

progression can also be classified with tumor stages in clinic, according to the dissemination of

the tumor. A tumor stage is decided by the size of a primary tumor, dissemination of lymph node

and number of metastasis [4].

It is now widely accepted that a tumor is derived from normal cells. The transformation of

normal cells into highly malignant derivatives is a multistep process of genetic alterations [5,6].

Six hallmarks of alterations in tumor cell physiology was proposed and summarized by Hanahan

and Weinberg in 2000: self-sufficiency in growth signals, insensitivity to growth-inhibitory

(anti-growth) signals, evasion of programmed cell death (apoptosis), limitless replicative

potential, sustained angiogenesis, and tissue invasion and metastasis [7]. And two emerging

hallmarks were added to this list in 2011: reprogramming of energy metabolism and evading

immune destruction [8]. Besides the genetic alterations, the dimension of in vivo complexity

(i.e., tumor microenvironment) draws more attention in these years. Tumors have increasingly

been recognized as organs whose complexity approaches and may even exceed that of normal

healthy tissues [8]. Tumor cells and the non-transformed cell types such as endothelial cells,

fibroblasts, and immune cells interact together to form their ‘joint tissue’, and modulate the

tumor microenvironment. It is characterized, inter alia, by oxygen depletion (hypoxia, anoxia),

glucose and energy deprivation, high lactate levels, glucose and bicarbonate deprivation, energy

impoverishment, significant interstitial fluid flow, etc [9]. As the tumor cells co-evolve with

their microenvironment, heterogeneity of cancer cells is observed in a single tumor tissue. From

this perspective, some of the phenotypic, genetic and epigenetic diversities observed at the cell

population level are likely to be natural consequences of tumor-microenvironment interactions

[10,11]. During the tumorigenesis and progression, several characteristics of the tumor

Introduction

2

microenvironment can be profiled: (1) an abnormality of tumor microvessels during

angiogenesis; (2) transformed energy metabolism according to the tumor cell proliferation and

local microenvironment changes; (3) local pH changes in hypoxic and necrosis region; (4) the

occurrence of hypoxic region owing to the imbalance between supply and consumption of

oxygen as the tumor grows; (5) evading of immune destruction; (6) heterogeneity of tumor

microenvironment.

Angiogenesis plays a significant role during tumor progression [12], as it is recognized that no

solid tumor can grow beyond size of approximately 1 mm3 without sufficient vascular supply

[13]. Tumor angiogenesis develops from an existing vasculature, through endothelial cell

sprouting, proliferation, and fusion. Moreover, it is proved that various types of cells also

contribute to the process, including endothelial cells [14], circulating endothelial precursor cells

[9], local mesenchymal cells [14], tumor cells [15,16], and even mononuclear cells [13].

However, tumor’s microvessels show severe structural and functional abnormality compared to

that of normal tissue [9]. More specially, tumors express an individual microvascular

architecture, which is often dilated, tortuous, elongated, and saccular [9]. Parameters defining

the microvascular unit, such as intervascular distance, interbranching distances, and branching

angles show tumor-type-dependent significant differences as demonstrated [17]. The

irregularities of tumor microvessels around a tumor are non-homogenous, highly and poorly

vascularized areas can be located directly to each other, although the vascular density in central

tumor areas is nearly as high as in the surrounding normal tissues [14]. Tumor microvasculature

significantly influences the tumor physiology as it is crucial for the nutrient and oxygen supply

and waste removal, such as oxygenation and glucose metabolism.

During the tumorigenesis and progression, the energy metabolism is adapted toward glycolysis

even in the presence of sufficient oxygen, leading to an augmented glucose uptake in tumors.

This aerobic glycolysis of the tumor is known as the ‘Warburg effect’ [18], a metabolic pathway

that has lower energy generation efficiency than an aerobic respiration. Although the aerobic

glycolysis is an inefficient way to generate adenosine 5 -́triphosphate (ATP), it is conducive to

the fast proliferation of tumor cells in adaption to the vast bio-synthesis needs of carbon bones

[19]. The increased glycolysis is realized via the overexpression of the glucose transporters

(GLUTs) and related enzymes (such as hexokinase), and/or enzyme activities changes. The

aerobic glycolysis converts glucose into pyruvate. Then, the majority of the pyruvate turn into

the lactate production rather than the oxidative phosphorylation. The lactate accumulates in the

tumor and leading to tumor acidity [20]. The lactate production and uptake are facilitated by

lactate transporters-monocarboxylate cotransporters (MCTs) [20]. In several tumor types, the

production and utilization of lactate can occur simultaneously in the tumor microenvironment

[21,22]. The lactate accumulation has been proven to be positively correlated with tumor

Introduction

3

progression and metastasis, implicating a poor overall survival of patients [23,24]. Tumor

metabolism involves complex interactions. It is not only affected by the metabolites, ions, the

vascular network and signaling cascades, but also affected by the oxygenation states of the tumor

microenvironment [20].

Advanced solid tumor is characterized with a lack of oxygen (knows as a hypoxia), where the

oxygen partial pressure (pO2) is lower than the surrounding normal tissues [25]. The low oxygen

supply arises from the structural and function abnormalities of the tumor micro-vasculature

network [9]. Although both normal and most tumor cells die due to the impairment in cell

proliferation, small population of tumor cells can survive by triggering adaptions in proteome

and genome [26], and further increase the malignance of the tumor progression. Transcription

factor hypoxia induced factor -1 (HIF-1) is a key regulator. HIF-1 regulates more than 100 genes

for erythropoiesis, angiogenesis, glucose metabolism switch, cell proliferation and apoptosis,

facilitating the adaption and survival of tumor cells in hypoxia condition [27].

Immunohistochemical analyses have demonstrated an increased HIF-1α levels with increase of

the tumor malignance [28]. Hypoxic regions play an active role in tumor malignancy, and have

become an independent prognostic factor for poor clinical prognosis [29]. Hypoxic tumor tissue

displays increased resistance to radiation and drugs [30,31], increased invasive clones [32], and

a very low overall survival rate of the patients with hypoxic tumors [31].

Intratumor heterogeneity is observed during tumor progression [33,34]. This is mainly due to

the local selection pressure of the tumor microenvironment. The local pressure, local pH and

pO2, mainly derive from the vasculature network, and in turn affect tumor cells’ metabolism,

genome and proteome. Hypoxic regions within tumors contribute to tumor heterogeneity in at

least three ways [35]. First, the adaption of tumor cells contribute to mutator phenotypes and

might enhance tumor evolution to more aggressive phenotypes [32]. Second, hypoxic

environments can directly regulate the epigenetic state of tumor cells. The anaerobic glycolytic

metabolism from hypoxia region generates and exports lactic acid, which in turn acidifies the

hypoxic region [36]. The low oxygen and low pH pressure in hypoxia region trigger tumor cell

cycle arrest and quiescence [26,36], thus increasing the phenotypic heterogeneity. Third, the low

vascularization of hypoxia regions reduces the local concentrations of drugs, which favors the

selection of drug resistant clones [37]. Moreover, the heterogeneity is another factor for therapy

and drug resistance [38].

Introduction

4

1.2. Molecular Imaging

1.2.1. Overview of molecular imaging

Molecular imaging originates from the field of nuclear medicine and radio-pharmacy for better

understanding the molecules’ information inside the living organisms in a noninvasive manner

[39]. Molecular imaging implies the integration of various imaging techniques, basic

cell/molecular biology, chemistry, medicine, pharmacology, medical physics, biomathematics,

and bioinformatics into a new imaging paradigm [40]. The entire molecular imaging research

chain is driven by molecular targets [41]. It usually exploits specific molecular probes as well

as intrinsic tissue characteristics as the source of image contrast, and enables the visualization

of the cellular function and the follow-up of the molecular process in living organisms without

disturbing them [42].

A typical process of molecular imaging includes [43]: (1) finding a molecular target depending

on a given biological question; (2) selecting a signal providing molecular probe (probe) or a

radiotracer (tracer) to trace the molecular target. The probes or tracers can be small molecules,

peptides, aptamers, engineered proteins or even nanoparticles. The relation between the tracer

and target could be direct-labeling, analogs, interaction; (3) introducing a probe or tracer into

the living subjects (in vitro or in vivo) and detecting the signal with appropriate imaging modality.

The in vitro tests provide better understanding of the probe’s properties and its interaction with

cells. The in vivo tests allow imaging the bio-distribution of the probe in both spatial and

temporal dimensions, which are useful for pharmacokinetics studies. The administration routes

and probe injection masses have to be optimized to achieve the desired signal with a high signal-

to-background ratio; (4) processing imaging analysis and applying pharmacokinetic modeling

for visualization and quantification. Various algorithms have to be developed for the image

reconstruction and model building; (5) exploring and validating the physiological/pathological

features underlying the imaging; (6) translating the molecular imaging strategy into the clinical

application.

With the development of imaging techniques, a variety of molecular imaging modalities are

available, with three prerequisites [44]: (1) high sensitivity enough to monitor interactions at a

molecular level; (2) sufficiently high spatial resolution to image mouse models of human disease;

and (3) available target-specific molecular probes. A thorough summary of the imaging

modalities and systems are well reviewed and shown in table 1 (with some modifications)

[40,42,44,45]. The imaging modalities include positron emission tomography (PET), single

photon emission computed tomography (SPECT), fluorescence molecular tomography (FMT),

Introduction

5

magnetic resonance imaging (MRI), intravital microscopy (IVM), bioluminescence imaging

(BLI), Ultrasound imaging, fluorescence reflectance imaging (FRI), X-ray computed

tomography (CT), positron imaging and other emerging new molecular imaging methods. These

imaging modalities have differences in spatial and temporal imaging resolution, imaging depth,

and imaging agents, and are oriented toward a variety of illnesses.

Table 1.1 Overview of imaging systems [42,44]

Technique Resolution Depth Time Imaging agents Target* Primary animal use

MR 10-100 µm No limit Minutes-

hours

Gadolinium,

dysprosium, iron

oxide particles

A, P, M Versatile imaging modality

with high soft-tissue

contrast

CT 50 µm No limit Minutes Iodine A, P Lung and bone imaging

Ultrasound 50 µm Millimeters Minutes Microbubbles A, P Vascular and

interventional imaging

PET 1-2 mm No limit Minutes 18F, 11C, 15O P, M Versatile imaging modality

with many different tracers

SPECT 1-2 mm No limit Minutes 99mTc, 111In

chelates

P, M Commonly used to image

labeled antibodies,

peptides and so on

FRI 2-3 mm < 1 cm Seconds-

minutes

Photoproteins

(GFP), NIR

fluorochromes

P, M Rapid screening of

molecular events in

surface-based tumors

FMT 1 mm < 10 cm Seconds-

minutes

NIR

fluorochromes

P, M Quantitative imaging of

targeted or ‘smart’

fluorochrome reporters in

deep tumors

BLI Several

millimeters

Centimeter

s

Minutes Luciferins M Gene expression, cell and

bacterial tracking

Introduction

6

IVM 1 µm < 400 - 800

µm

Seconds-

minutes

Photoproteins

(GFP),

Fluorochromes

P, M All of the above at higher

resolutions but at limited

depths and coverage

PI 200-500

µm

< 1 mm Seconds-

minutes

18F, 99mTc M Tumor metabolism and

angiogenesis [46]

*Primary area that a given imaging modality interrogates: A, anatomical; M, molecular; P, physiological.

BLI, bioluminescence imaging; CT, X-ray computed tomography; FMT, fluorescence-mediated molecular

tomography; FRI, fluorescence reflectance imaging; GFP, green fluorescent protein; NIR; near-infrared; MR,

magnetic resonance; PET, positron emission tomography; SPECT, single-photon emission computed tomography;

IVM, intravital microscopy, PI, Positron imaging.

1.2.2. Positron imaging

The positron is the antiparticle counterpart of the electron, having the same mass as the electron

but carries a positive charge. Positron particle was first proposed in theory by Paul Adrien

Maurice Dirac [47] in 1928, and then proved in experiment by Carl David Anderson [48,49] in

1932. When a positron collides with an electron, annihilation occurs, producing two photons

which are emitted simultaneously in opposite directions (i.e., gamma ray). Based on detection

of these pairs of photons emitted indirectly by a positron-emitting radionuclide, PET was

developed. PET has a very high sensitivity for a three-dimensional imaging of functional

processes in the body with a very small mass amount of tracer, however, the resolution of the

imaging is still limited (~1 mm). This is mainly because the gamma ray is detected instead of

the direct detection of the positron signal. A much finer resolution may be achieved by using

direct positron signal, and the information about beta particles labeled molecules may also be

directly imaged. As positrons annihilate once they collide with electrons, positron imaging is

mainly applied with a contact imaging method. Conventionally, autoradiography is utilized to

detect both the positron and gamma signals via direct contact and exposure to a phosphor film,

then reading out the phosphor films. This is applied for in vitro imaging of radioactive slides

immediately after scarification of the animal with tracer inside. For the quantitative detection of

beta particles indirectly, a scintillation solution is added to the tracer and the scintillation is

detected. Another indirect way available for detection of the beta particles is based on the

phenomenon called Cerenkov radiation, in which the visible light emission generated by a

charged particles traveling through an optically transparent material is specially detected [50].

Cerenkov radiation also allows in vivo optical imaging of positron emitting radiotracers [51].

Introduction

7

With the development of digital autoradiography [52], various digital detectors like position

sensitive avalanche photodiode (PSAPD) [53], complementary metal oxide semiconductor

(CMOS) [54] and charged-coupled detector (CCD) with scintillator [55] have appeared, making

direct detection of positrons possible. Recently, a positron camera with the hybrid silicon pixel

device Timepix (CRYTUR, spol. s r.o.) was jointly developed by Crytur, spol. s r. o. and our

group. The details of the basic performance for direct positron measurement with this detector

were reported in [56,57]. The development of the detector provides an alternative way to

perform autoradiography, which is based on direct contact imaging [58]. Direct contact detection

perfectly fits with microfluidics, which minimizes and integrates all the operation and detection

on a single microfluidic chip [59] (see 1.3.2). The feasibility of this idea has been proved by

pioneering studies: For example, Vu et al. [60] developed a positron imaging system integrating

a beta camera and a microfluidic chip, which is capable of positron imaging of cellular 18F-FDG

uptake on a microfluidic chip. Dooraghi et al. [61] further developed this detection system using

a PSAPD detector. This system called Betabox and is successfully applied in real time

metabolism detection and drug screening [62].

Not only applied in in vitro study, the direct positron imaging is also applicable for in vivo

imaging, with the aid of a tissue preparation technique called window chamber model (see 1.3.3).

A pioneering study was performed by Zhonglin Liu et al. [46]. They combined a PSAPD

detector with a mouse dorsal skin window chamber model for imaging, which enabled direct

positron and electron imaging of tumor microenvironment. In the following, our group also

designed a multimodality imaging compatible window chamber system enabling direct positron

imaging [63].

1.2.3. Intravital imaging

Intravital imaging can reveal cellular responses over time and space and can be conducted under

conditions closely approximating those of a natural environment [64]. Intravital imaging is

mainly denoted as intravital microscopy (IVM), imaging of live animals at microscopic

resolution. Since 1800, IVM has been applied as a tool to study the morphology of microvessels

and tissues in living animals [65]. Nowadays, IVM is largely based on the detection of

fluorescence [66] and primarily used to study the location, motility, adhesion, and interactions

of individual cells in three physical dimensions over time [64]. The emergences of optical

frequency domain imaging (OFDI) [67], phosphorescence lifetime imaging (PLI) [68], spinning

disk confocal imaging [69] and multiphoton (MP) imaging [70] open up the field of IVM to

imaging in a larger number of channels, at greater depths, for more extended periods of time,

over larger tissue areas, and at vastly improved subcellular resolutions [64]. For example,

Introduction

8

Dewhirst et al. [71] applied fluorescence microscopy and PLI on a rat window chamber model

for visualize the location and number of arterioles and their pO2 values, Rofstad et al. [72]

developed first-pass imaging method with fluorescence video microscopy for studying blood

flow in tumors and normal tissues in mouse dorsal window chamber model.

Imaging modalities with larger imaging dimension, like ultrasound [73], MRI [74], positron

imaging [46], bioluminescence [70] and implantable biosensors [75], can also be applied to

broaden the scope of the intravital imaging. Furthermore, multi-modalities imaging is applied

for intravital imaging. Multimodal imaging with window chamber explored by Gaustad et al.

[74] disclosed a method of combining dynamic contrast-enhanced MRI (DCE-MRI) with

fluorescence microscopy to establish the correlation between DCE-MRI-derived parameters

with tumor vascularity. Confocal laser scanning microscopy (CLSM) and DCE-MRI showed

complementary information for tumor microvascular structure and permeability study [76].

1.3. Linking of molecular imaging and tumor biology

Molecular imaging attempts to characterize and quantify biological processes at the cellular and

subcellular level in intact living subjects in non-harmful manner [77], and is closely related to

the pharmacokinetics. It has not only been widely applied in imaging live subjects to characterize

fundamental biological processes [40], but also in molecular oncology investigations, including

tumor and host response, tumor invasion and metastasis, matrix remodeling and angiogenesis,

cell death, clinical detection of epithelial neoplasia and intra-operative imaging [42]. However,

it is challenging to figure out the underlying physiological/pathological features with molecular

images obtained. In general, image acquisition, processing, computation, and in vitro

immunohistochemical validation have to working together to link the molecular imaging with

the underlying tumor physiological features. A common computing platform is therefore the key

to assemble and fuse different imaging techniques for screening, detection, characterization (in

vivo pathology) and real-time treatment of early-stage cancers [42].

Molecular imaging uses probes to help image particular targets and pathways [41]. A selection

of appropriate tracer related to the biology question is crucial, as the entire molecular imaging

research chain is driven by molecular targets [41]. A tracer, either a direct radiolabeled version

of a naturally occurring compound, an analog of a natural compound, or an unique radiolabeled

compound, is designed to provide information about a particular physiological function of

interest, such as blood flow, a metabolic process, a transport step, a binding process, etc [43].

Usually, the naturally occurring compound has a very complex biochemical fate, making the

model describing the tissue radioactivity data of a directly radiolabeled compound quite complex.

Introduction

9

A well-designed analog can dramatically simplify the modeling and improve the sensitivity of

the model to the parameter of interest. An analog is a compound whose chemical properties are

slightly different from its natural compound [43].

The selection of tracer 18F-FDG (2-deoxy-2-(18F)fluoro-D-glucose) [78,79], an analog of

glucose, for glycolysis analysis is a typical example. 18F-FDG PET is widely used for in vivo

tumor imaging based on the increased glycolysis in tumor cells known as Warburg effect [80,81].

The uptake of 18F-FDG is considered as a biomarker of tumor malignancy and of prognostic

value for therapy management. 18F-FDG has similar transport and glycolysis kinetics to glucose.

Both 18F-FDG and glucose enter cells by the same transport enzyme, and can be phosphorylated

by the enzyme hexokinase. After being transported into the cells, glucose is phosphorylated into

glucose-6-phosphate and further metabolized through the glycolysis pathway, while

phosphorylation of 18F-FDG into the 18F-FDG-6-phosphate is trapped in cells and cannot be

further metabolized (18F-FDG-6-phosphate is not a substrate for the next enzyme in the

glycolytic pathway) [82]. When investigating the mechanism of 18F-FDG for tumor diagnosis

and therapy response, the uptake of 18F-FDG is usually interpreted with regard to the expression

of GLUTs and HKs of the pathway. Among the two families of proteins, glucose transporter-1

(GLUT1) and hexokinase-II (HK2) are mostly investigated [83-86]. GLUT1 expression is

usually considered to be associated with malignant tumor stages [87]. 18F-FDG uptake was

shown to be more influenced by GLUT1 than other subtypes of GLUTs in many tumor tissues

such as breast cancer [88], pancreatic tumor [84,89] and cervical cancer [90]. HK2 directly

mediates glycolysis and promotes tumor growth [91] and it has been found to be the predominant

isoform of HKs in many tumors [83,92]. The overexpression of HK2 has been reported to

increase 18F-FDG uptake in cancer cells [93]. The overexpression of GLUT1 and HK2 is also

related to the tumor proliferation and stages, showing resistance to drug treatment and poor

prognosis [94,95]. The GLUT1 and HK2 have been employed as therapeutic target for drug

development associated with cancer metabolism [96,97]. On the other side, the expression of

GLUT1 and HK2 varies among different types of cancer cells [86] and tumors possess special

abilities to adjust the expression of GLUT1 and/or HK2 to maintain their energy supply and

homeostasis[93,98]. Furthermore, among the two families of proteins, other subtypes of glucose

transporter [84,90,96,99-101] and hexokinase [83,91,100] are also significantly overexpressed

in most of tumor tissues. For example, GLUT3 is often overexpressed in brain tumor and lung

cancer. GLUT2 is found overexpressed in HCC. GLUT12 is found to be overexpressed in breast

cancer. Many studies show that these subtypes are also correlated with the 18F-FDG uptake

[102,103]. Hence, it is very difficult to investigate the adaption of their functions under various

metabolic conditions [104,105].

Introduction

10

1.3.1. Kinetic modeling

Pharmacokinetics (PK), refers to a time-dependent concentrations of a substance in a living

system [106]. It describes the absorption, distribution, chemical changes (metabolism) and

excretion of a specific drug (such as a radiotracer) and its metabolites after administration into

the body [43,107]. Pharmacodynamics (PD), different from pharmacokinetics, refers to the

pharmacological effect of a drug to a living system. It describes the pharmacological effect (e.g.,

death of tumor cells in response to a chemotherapeutic agent), from a drug concentration on the

target site [108]. Here the pharmacokinetic modeling is introduced in detail.

Overview of modeling

The purpose of a mathematical model is to define the relationship between the measurable data

and the physiological parameters that affect the uptake and metabolism of the tracer [43]. A

kinetic model describes the bio-distribution and kinetics of the imaging signal measured under

suitable imaging conditions, taking into account the radiopharmaceuticals of a particular

radiotracer [43].

Factors relating to the development of a kinetic model include physics, pharmaceutics, and

mathematical assumptions. When an appropriate tracer is applied and suitable imaging

conditions are performed, the tissue radioactivity signal is affected by the local tissue physiology

(such as perfusion and metabolism) and the input function (the time-course radioactivity in the

plasma). As such, a model is applied to describe the relationship between tissue radioactivity

and the controlling factors. A model could predict the tissue radioactivity concentration along

the time, or more usefully, be used to estimate physiological parameters by inverting its

equations. The method to invert the model equations and solve for physiological parameters is

called model-based modeling [43].

The typical process of modeling can be described as five main steps [43]: (1) providing a priori

information based on the characteristics of the tracer to specify a complete model; (2)

performing initial modeling studies to define an identifiable model; (3) performing validation

studies to refine the model, verify its assumptions, and test the accuracy of its estimates; (4)

drawing a practical model; (5) further optimizing the practical model and analyzing errors to get

a model-based method, which is both practical and reliable, producing accurate physiological

measurements.

Depending on the modeling target and the complexity, modeling could be roughly classified into

three groups: Stochastic (non-compartmental) models, compartmental models and distributed

models. Stochastic models require minimal assumptions concerning the underlying physiology

of the tracer’s uptake and metabolism [109]. There are no specific compartments needed to be

Introduction

11

explicated for tracer molecules. Certain physiological parameters, such as volume of distribution

and mean transit time could be extracted [43]. The compartmental models define some of the

details of the underlying physiology, but do not consider concentration gradients [43]. Here,

compartments are defined as volumes with homogeneous tracer distribution and tracer’s kinetics

into and out of each compartment are assessed by the models. Compartmental models are

especially useful for tracer kinetic analysis in PET imaging, where the time-concentration curve

of the tracer in blood and urine is measureable [43]. The distributed models are the most complex

models, which not only specify the possible physical locations and biochemical forms of the

tracer, but also include the concentration gradients within different physiological domains [43].

They are mainly used for diffusion [110] and capillary-tissue exchange studies [111], where the

tracer’s precise distribution is concerned.

To compromise the complexity of the physiological and limitations of realistic measurement

conditions, assumptions have to be properly made. Here, some of the general assumptions or

facts for PET tracer models are listed (to note, the assumptions are not always true, and in some

cases may be violated) [112]. First, modeling generally assumes that tracer levels are appropriate.

As the tracer is presented in the tissue at negligible mass concentrations, little or no change in

the saturation of relevant enzymes or receptors occurs. Second, the physiological processes and

molecular interactions are not influenced by the PET measurement. Third, constant state (steady

state) assumption states that the physiological processes and molecular interactions are in a

constant state during the PET measurement.

Moreover, the tracer is crucial for establishing a model. Since the kinetic parameters of an analog

are still different from the natural compound, the relationship between the radioactive analog

and the native compound has to be determined. The relationship between 18F-FDG and glucose

is described by the lumped constant [113-115]. The uptake and distribution of the tracer are not

only affected by the physiological process under study, but also affected by other factors. Take 18F-FDG for example, in an in vivo situation, regional radioactivity concentration data along the

time is also affected by regional blood flow (perfusion), tracer clearance from blood, tracer

metabolism, and regional uptake of any radioactive metabolites.

Compartmental modeling

Kinetic models have been developed in order to simplify the description of the processes during

the interaction between a tracer and an organism. Compartmental modeling is the most

commonly used method for mathematical describing the uptake, distribution and the clearance

of radioactive tracers throughout the body [116-118], and gives the best approximation to reality.

Introduction

12

Here, compartments are defined via a boundary setting, in which boundary can be either a

physical (for example, a cell membrane, a blood vessel wall, brain blood barrier) or chemical

state (i.e., bound and unbound receptor) according to the properties of the tracer and the

physiological process it involves. These boundaries partition the measured tracer activity

concentration in tissue into distinct compartments. The compartments are typically numbered

for mathematical notion. In a compartmental model, all molecules of a tracer will be specified

at any given time to be in one of many compartments. Besides, the possible transformations of

the tracer among the compartments are defined with the model. This fractional rate of changes

in tracer concentration among compartments is called rate constants, with the symbol k, the units

is min-1.

Compartmental modeling has some assumptions for simplification in mathematics (not always

true, and under some situations maybe violated) [43]. First assumption is called instantaneous

mixing assumption, which states that the concentration in different compartments is

homogeneous. There are no concentration gradients within a single compartment. Hence, all

molecules in a given compartment have equal probability of exchange into other compartments.

Second, the underlying physiological processes are in steady state. Last, to generate the

equations of a model, the amount of tracer moving from compartment A to compartment B per

unit of time must be defined.

In a PET measurement, after the injection of a tracer into the blood stream, both the tissue tracer

concentration and the blood concentration are measured over time [43]. For a tracer distribution

process, the transport and binding rates of the tracer will be determined by the regional

concentration differences. In the PET measurement, a region of interest or pixel can be analyzed

independently. Thus, the model can be applied to a specific region or pixel to determine local

physiological parameters. The physiological interpretation of the source and destination

compartments define the meaning of the rate constants for movement of tracer between them.

In PET, a two-tissue-compartment model is widely used for 18F-FDG PET measurement analysis

[119].

1.3.2. Microfluidic radioassay

Microfluidics is the science and technology of systems that process or manipulate small (10-9 to

10-8 liters) amounts of fluids, using channels with dimensions of tens to hundreds of micrometers

[59]. Several terms are similar to the microfluidics by different researchers from different fields

or/and during the different stages of the development, such as microelectromechanical systems

(MEMS) [120], micro total analytical systems (μ-TASs) [121] and lab-on-a-chip. The

microfluidics was originally developed with the needs from the fields of molecular analysis,

Introduction

13

biodefence, molecular biology and microelectronics [59]. Microfluidics with advantages of low

cost for samples and reagents, in-situ and real-time analysis has been widely applied in analytical

chemistry, chemical synthesis, cell biology, molecular biology and pharmaceutics. Typically, a

microfluidic system composes of a microfluidic chip, a fluids operation component (pump,

valves, tubing etc.) and a detecting unit. A microfluidic chip can be made from a variety of

materials, such as silicon, Mylar, paper, Teflon, polydimethylsiloxane (PDMS) and

polycarbonates. The fabrication methods for a microfluidic chip is developed fast, from early

silicon technology which takes months for fabricating one microfluidic chip to an evolutional

soft lithography technology with PDMS which takes about one or two days [122,123].

Nowadays, a variety of prototypes and commercial microfluidic chips are supplied by many

companies [124-126]. Inside a microfluidic chip, several components may be involved:

microchannel, micro chamber, micro valves and mixers [127], microreserviors, microelectrodes,

microdetection components, and microports and connectors [128].

The fluids in the microchannel have some special characteristics and properties, such as laminar

flow, electro-osmotic flow (EOF) [129] and optical waveguide [130]. A laminar flow works

occurs during the mixing of two fluid streams instead of turbulence on a microfluidic channel.

Two fluid streams would not mix to each other in the microchannel but flow in parallel, and

only the molecular diffusion works for the mixing. The EOF occurring in the microchannel

would drive the fluids moving a plug rather than with parabolic-flow profile in macroscopic

scale, which is useful for electrophoretic separations of deoxyribonucleic acid (DNA) in

microchannel [131]. Another specific phenomenon shown in the microchannel is the optical

waveguides, which derives a laminar liquid comprising a high index of refraction flowing

between two streams of low-index ‘cladding’ in the microchannel [130]. This characteristic

enables a new detection methods for refraction waveguides detection based on the microfluidics

[132].

Microfluidics is a minimized technique, so that a variety of detection methods can be integrated

for detection. The detection methods in microfluidics are mainly classified into two types:

optical methods and electrochemical methods. In detail, optical methods include absorbance

detection, fluorescence detection, chemiluminescence and bioluminescence detection, Raman

spectroscopy detection, refractive index detection, thermal lens microscopy detection, and

surface plasmon resonance detection [133]. Electrochemical methods [134] include

amperometry, potentiometry, conductometry and electric impedance [135,136]. Other detection

methods, such as mass spectrometry, magnetic resonance spectroscopy, and acoustical methods,

are also available. There is a huge amount of insightful review papers every year, showing that

radiometric detection has not been studied much.

Introduction

14

Figure 1.1 Schematic diagram of the cross section of a typical microfluidic chip and the

PSAPD detector. Image reprinted from Vu et al. [137]

With the development of small planar radiometric detectors like PSAPD [53,137] and Medipix2

silicon pixel detector [54], radiometric detection in connection with microfluidic was become

available. The minimized radiometric detection shows some advantages in applications like

kinase activity radioassay [138], imaging of the glycolysis in cells [60,139] and drug

intervention study [62]. In general, microfluidic chip based radiometric imaging systems could

be termed as micro radioassay system [60] or Betabox [61]. Typically, the system contains a

microfluidic chip, a syringe pump and a radiometric imaging detector. As the detector is located

directly beneath the microfluidic chip, the micro radioassay of cells are detected in situ (see

Figure 1.1). Vu et al.[60] applied 18F-FDG to imaging the glycolysis in 4 different melanoma

cells lines on chip, supplying a new way to perform conventional well-type 18F-FDG uptake

with on-chip cell culture detection. Advantages are smaller cell population (hundreds) and

parallel samples detected in the same batch time. In parallel, kinetic modeling experiments were

performed and kinetic strategies of positron imaging were studied [139]. And this system was

further advanced via better microfluidic chip design for metabolic response study with drug

intervention [62]. In these applications, the pooled radioactivity is removed via washing steps,

and imaging of the cells is performed after the wash steps, such that only the retention

radioactivity inside the cells is detected. Fang et al. applied a solid-state beta-particle camera

imbedded directly below the microfluidic device for real-time quantitative detection of the signal

from a kinase activity radioassay [138]. This method was an adaption of the conventional kinase

activity radioassay, with an obvious reduction of the chemical agent and radioactivity costs, and

much less sample requirement. Only thousands of cells were needed for on chip cell culture and

for the whole micro radioassay, while the traditional test tube-based needed cell number of 107.

1.3.3. Window chamber

Window chamber is an effective tissue observation apparatus for intravital imaging [140]. It sets

up observable tissue microenvironment between or behind a fixed transparent window on an

Introduction

15

intact animal, providing a window into the underlying tissue, which enables in-depth

longitudinal observation of tissue physiologies. The development of the tissue can therefore be

monitored over the course of several days to weeks within a realistic in vivo model with proper

imaging techniques. First reported in 1928 by Sandison [141], the window chamber originally

was utilized to investigate angiogenesis in the normal environment of a rabbit’s ear. In 1939,

window chambers were first reported in the context of investigating cancer by Ide et al. [142].

Since then, various chambers have been developed and implanted with the aim to investigate

microcirculation [143], angiogenesis [144], hypoxia [72,145], molecular dynamics [68,71,146]

and therapeutic interactions [147]. Based on tissue preparations, window chamber can be

generally classified into two categories: acute window chamber and chronic window chamber.

Acute window chamber, including hamster cheek pouch, mesentery, liver or pancreas chamber,

allows the observation of orthotopic tumors. However, it does not support repeated or long-term

observations. Chronic window chamber allows continuous noninvasive, long-term monitoring

of tissue pathologies. Typical examples are ear chamber, dorsal skinfold chamber, percutaneous

one-sided window chamber [148], abdomen window chamber, cranial chamber [143], hamster-

cheek-pouch-window and spine cord chamber.

The dorsal skinfold window chamber has been most commonly used to investigate basic cancer

biology and tumor development in rodent models. This technique involves surgically implanting

a pair of window plates to support a transparent window. The dorsal skin of a mouse is folded

up into the frame, and one side of the skin is removed in a circular region of ~ 1 cm in diameter.

Furthermore, a round coverslip is placed over the opening, thereby enabling imaging. During

window implantation, tumor cells (typically 3-10 mm in diameter and 100-500 µm thick

[68,140]) can be injected into the underlying skin tissue. Over the course of several days, the

tumor will grow in the window, enabling longitudinal imaging of tumor initiation and growth.

Quantitative image analysis can then be used to quantify the presence of reporter genes, optical

probes, hemodynamics and other parameters in both normal and tumor tissue. There are also

some disadvantages of this technique that are important to be considered. In particular, the dorsal

window chamber requires a tumor to grow subcutaneously rather than orthotopically. The

window chamber is also confined such that tumors cannot grow larger than approximately 7-8

mm in diameter without overgrowing the window chamber. Moreover, the window cannot be

maintained for longer than ~2 weeks before either the tumor grows too large or the window

begins to deteriorate. However, this technique is uniquely suited for applications requiring

longitudinal, high-resolution imaging of dynamic processes. In vivo tissue morphological and

metabolic characteristics can be obtained from intravital imaging on window chamber in the

studies about tissue pathologies, such as tumor hypoxia [149], tumor cell-induced angiogenesis

[150], impact of longitudinal oxygen gradients on tumor hypoxia [68], molecular dynamics

Introduction

16

[151], dynamic interaction of biomaterials with their surrounding host tissue [152] and

therapeutic activity of anticancer drug [153].

1.4. Goals of the study and overview of this thesis

1.4.1. Goals of the study

Objective 1: Develop and establish a microfluidic radioassay system for real-time investigation

of PET tracer pharmacokinetic in tumor cells.

To investigate the physiological interaction of the PET tracer with cells is valuable for the

interpretation of molecular imaging and for the characterization of the specific PET tracer and

pharmaceuticals.

Typically, the investigation of cellular tracer uptake is based on the discontinuous measurements

at specific time intervals [154]. Although the uptake is essentially a kinetic and continuous

process, it is impossible to obtain the real time tracer uptake profile with this method. Recently,

a portable in vitro molecular imaging system called micro radioassay or Beta-Box [60-62] was

developed, which integrated a β-particle imaging camera with a microchip, and provided a

potential way to monitor cellular uptake process in real time, as the detector could monitor cells

in situ. However, to minimize background noise due to residual tracer in the extracellular

solution, a washing step is necessary for this system. The imaging was performed only after

washing just like the typical method. The information about tracer uptake is therefore also

“static”.

The first aim of this thesis is to develop and establish a microfluidic radioassay system

named continuously infused microfluidic radioassay system for real-time investigation of

PET tracer pharmacokinetic on tumor cells.

Objective 2: Develop and establish a multimodal intravital molecular imaging system to explore

tracer uptake of tumor in vivo

Living organisms are extremely complex functional systems. This complexity is a great barrier

to the identification of interactions between tracers and the exploration of their biological

functions. Multimodality imaging has become an attractive strategy for in vivo studies to harness

the strengths of different imaging methods. Window chamber models sets up observable tissue

microenvironment between or behind a fixed transparent window on an intact animal, which

enables in-depth observation of tissue physiologies.

Introduction

17

The second aim of this thesis is to develop and establish a high-resolution multimodal

intravital imaging system based on dorsal skin window chamber tumor model.

1.4.2. Overview of this thesis

The thesis is organized in 8 chapters.

Chapter 1 gives an overview of tumor biology, molecular imaging, and the linking of these two

subjects by briefly introducing the basics of techniques and the state of the art of the

methodologies. It is a background knowledge preparation for the introduction of this thesis. The

aim of the study is addressed in the last section.

Chapter 2, 3 and 4 present a continuously infused microfluidic radioassay (CIMR) system that

enables positron imaging of cellular tracer uptake in real time. Chapter 2, 3 and 4 introduce the

materials and methods, results and discussion separately. In this system, constantly infused PET

tracer medium flows over a reference micro-chamber and a micro-chamber with attached cells

simultaneously; Positron imaging of both micro-chambers is monitored in real time. With

extraction of real-time radioactivity signal of tracer medium and integrated signal with cells, the

cellular real time tracer kinetics was extracted. PET tracer 18F-FDG was applied for

establishment and validation of the method. The system detection ability was validated with the

comparison of conventional tracer uptake experiment. Furthermore, the cellular 18F-FDG uptake

kinetics was assessed with an adapted cellular two compartmental modeling. For an initial

validation, the kinetic modeling parameters denoting the 18F-FDG uptake related transporters

and enzymes were compared with the messenger ribonucleic acid (mRNA) expression of the

correlated GLUT1 and enzyme of HK2.

Chapter 5, 6, 7 present a multimodal intravital molecular imaging (MIMI) system based on a

multimodal compatible dorsal skin window chamber tumor model. Chapter 5, 6 and 7 introduce

the materials and methods, results and discussion separately.

First, a dorsal skin window chamber with fiducial markers was designed and fabricated, which

adapts to several imaging modalities including positron camera, MRI, fluorescence imaging and

mini optical sensor. Second, a rat dorsal skin tumor model was established via window chamber

implantation and tumor transplantation into the window area. Third, imaging protocol for each

imaging modality was established, considering of co-registration among different imaging

modalities. Several adapting tools enabling mounting to the multimodal window chamber were

developed to aid co-registration among different imaging modalities. Co-registration of imaging

from different imaging modalities was successfully attained. Last, the physiological feature

Introduction

18

related to the PET tracer imaging on the tumor microenvironment was explored with this system,

and the initial study proved the feasibility of the system.

Chapter 8 addresses the innovation of the two imaging systems and methods, summarises the

major development and contributions of this thesis.

Introduction

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250.

Materials and Methods for Continuously Infused Microfluidic Radioassay System

26

2. Materials and Methods for Continuously Infused

Microfluidic Radioassay System

The development and validation of a microfluidic radioassay system was one of the main

projects of this thesis. In the following chapters 2, 3 and 4, the system is described in detail. The

text is from our accepted manuscript [Zhen Liu*, Ziying Jian*, Qian Wang, Tao Cheng,

Benedikt Feuerecker, Markus Schwaiger, Sung-Cheng Huang, Sibylle I. Ziegler and Kuangyu

Shi. "A Continuously Infused Microfluidic Radioassay System for the Characterization of

Cellular Pharmacokinetics." Journal of Nuclear Medicine. (*: co-first author)], with additional

details.

In this study, the final version of the Matlab program code for the image processing was done

by Dr. Kuangyu Shi, and Dr. Qian Wang wrote the very first version of the Matlab code. All the

kinetic modeling program code in both Matlab version and Microsoft Visual C++ version was

provided by Dr. Kuangyu Shi. The Monte-Carlo simulation using Geant4 for depth-dependent

sensitivity correction of the positron camera was done by Dr. Qian Wang. The qPCR

experiments were performed by Ziying Jian and Tao Cheng.

A typical detection process for the CIMR includes: on-chip cell culture, on-chip CIMR

measurement and data processing and analysis.


27

2.1. Microfluidics

2.1.1. Continuously infused microfluidic radioassay (CIMR) system

Figure 2.1 Setup of the continuously infused microfluidic radioassay (CIMR) system: (A) a

photo of the microfluidic chip in operation; (B) a sketch of the tracer medium flow during the

measurement; (C) a side view at the line of view cut on panel b for the cell chamber during the

measurement; (D) an example frame from the positron camera during the CIMR measurement;

(E) an example of time-course events/pixel curves from the medium chamber (pink) and the cell

chamber (black).

The continuously infused microfluidic radioassay system is based on a microfluidic chip (µ-

Slide VI0.4, ibidi GmbH, Munich, DE), a flow control unit and a positron camera as shown in

Figure 2.1. The microfluidic chip consists of two parallel chambers (17 × 3.8 × 0.4 mm3 each),

each one with separate inlet and outlet. The two chambers are connected as shown in Figure

2.1B. One of them serves as a medium monitoring chamber and the other as a cell culture

chamber. The flow control unit is a programmable syringe pump (Cavro® XLP 6000, Tecan

Group Ltd. Männedorf, CH) with connecting flow tubes. The medium with radioactive tracer is

driven by the pump from the medium reservoir via the medium monitoring chamber into the cell

culture chamber (Figure 2.1B). The medium monitoring chamber and the cell culture chamber

were simultaneously measured by a positron camera during the measurements (Figure 2.1C).

The positron camera consists of a single-particle counting silicon pixel detector (300 µm

thickness, Crypix, Crytur Ltd. Turnov, CZ) bonded to a CMOS readout chip (Timepix, CERN,


28

CH). The field-of-view of the positron camera is 14 ×14 mm2 (256 × 256 pixels). Details of the

basic performance of this detector for direct positron measurement can be found in [1]. Figure

2.1D shows an example frame from the camera during the CIMR measurement, the frame width

is 1 min and the time of measurement is 40 min after starting the tracer infusion. In the image,

both chambers can be distinguished; the left chamber had less readout events (bright blue color)

while the right chamber had a larger number of readout events (red and yellow color). The left

chamber is the medium chamber and the right chamber is the cell chamber. The events-per-pixel

curves along the time taken from the medium chamber and the cell chamber are shown in figure

2.1E. The medium with radioactivity flows from the medium chamber to the cell chamber, so

the medium chamber shows the rise of the signal earlier than the cell chamber. The signal in the

medium chamber is constant while the signal is increasing along the time in the cell chamber.

2.1.2. Detection procedure

Cell culture preparation

Two types of human cancer cell lines, Capan-1 (pancreas adenocarcinoma) and SkBr3 (breast

adenocarcinoma), were selected for the investigation. Two days before the CIMR measurements,

approximately 1.5 × 104 cells in 30 µl single-cell suspension were inoculated into the cell culture

chamber of a microfluidic chip using a pipette. Cells formed a homogeneous single layer on the

bottom surface of the chamber. The cells were then cultured by DMEM medium (Biochrom

GmbH, Berlin, DE) with 25 mM glucose, 4 mM glutamine, supplemented with 10% fetal bovine

serum, 100 U/ml penicillin, and 100 µg/ml streptomycin in an incubator (Heraeus

Microbiological Incubator Series 6000, Thermo Scientific) with controlled condition of 37 ºC

and 5% CO2.

Measurements with the CIMR system

Before the infusion, the flow control unit was sterilized and cleaned using 70% ethanol and

ultrapure water. 18F-FDG was diluted into the cell culture medium to generate a radioactive

solution of 0.2-5 MBq/mL with the investigated glucose concentration. The tracer medium

solution was pumped through the medium chamber and cell chamber at a constant flow of 1.25

µl/s flow of 1.25 µl/s, which created a small shear stress (< 0.13 dyn/cm2, estimated based on

the application note of ibidi GmbH) near the chamber surface. This flow speed leads to a medium

refresh rate of 2.5 times per minute, which is sufficient to maintain a constant supply of 18F-

FDG in the micro chambers. Positrons emitted both, from the medium chamber and the cell

chamber, were measured by the positron camera for 75 min. Measurements were binned into

frames of 1 min each. Each measurement was repeated for 6 times.


29

Figure 2.2 Example of microscopic image after CIMR measurement for cell counting: (A)

imaging of the cells with a large field-of-view (FOV). The yellow boxes depict the selected

small FOVs for cell counting; (B) typical microscopy image with a small FOV (725 µm × 546

µm) for cell counting corresponding to the selected yellow box in (A).

After the CIMR measurements, the cell chamber was imaged using a microscope (BZ-9000,

Keyence Co Ltd, JP) with a phase contrast objective lens of 20. Five widely distributed

microscopic FOVs were selected for counting the number of cells (see Figure 2.2). The cells

imaged in the displayed FOVs are nearly homogeneously distributed. Thus, the average of the

cell numbers for 5 widely distributed FOVs is assumed to be representative for the cell number

density on the chip. In addition to the proposed method, several other cell counting methods

were tested, including (1) trypsin detachment of cells and then counting cell number after trypan

blue staining with manual counting as well as machine counting using Invitrogen CountessTM

Automated Cell Counter, Beckman Counter Vi-CELLTM and CASY Cell Counter and Analyzer;

(2) total protein analysis (BCA protein assay). The proposed method used and described in the

study showed the smallest variation for the samples inoculated from the same cell concentration

and in the same condition. The average cell number of the 5 FOVs was then normalized to the

cell number per detector pixel (55 × 55 µm2).

2.1.3. Image processing and normalization

The acquired dynamic data was corrected for radioactive decay. For each 1-minute frame, the

mean events per pixel (55 × 55 µm2) within a box region of interest (12 × 3.8 mm2) in the center

of an imaged chamber were calculated separately for the medium chamber and for the cell

chamber. Time activity curves (TACs) were generated to describe the changes of mean events


30

per pixel versus time. The calibrated radioactivity of the medium in the cell chamber was

estimated based on the measured TAC of the medium chamber after correction of delay and

dispersion. This can be described using the following formula [2]:

𝛽𝑐𝑚(𝑡) = 𝛽𝑚(𝑡 − Δ𝑇) ∗1

𝜏𝑒−

𝑡𝜏

where βcm and βm denotes, respectively, the event density curves of the medium in the cell

chamber and the medium chamber Δ𝑇 and 𝜏 are coefficients characterizing the delay and

dispersion of the FDG activity flowing from the medium chamber to the cell chamber and ∗

represents the convolution operator.

To estimate the delay and dispersion coefficients, the CIMR system was run 6 times without

cells in the cell chamber using the same fluidic condition as the real measurements. The

coefficients of the decay and dispersion were determined regularly, especially after changes of

the settings of the CIMR operating condition.

The microfluidic chamber has a height of 400 µm and the positron camera is placed below the

chamber. Thus, the positron camera had lower sensitivity for the positrons emitted from upper

layers than from bottom layers in the chamber. This depth-dependent sensitivity profile of the

positron camera was corrected in the measurements by multiplying a correction factor of 1.74

(see chapter 2.2.1).


31

Figure 2.3 Procedure of data processing


32

The events of the cells in the cell chamber were generated by subtracting the estimated medium

events in the cell chamber from the total events of the cell chamber (i.e., the events in the

medium was removed from the total measured events in the cell chamber). The FDG

concentration in the medium was computed by normalizing to the volume associated with a pixel

in the chamber (55 × 55 × 400 µm). The cellular FDG concentration was calculated using an

estimated mean cell number and mean cell volume (see Figure 2.3). The mean volume of

adherent cells with mean diameter d was approximated by the volume of an ellipsoid (𝜋𝑑3/12).

The cellular uptake was estimated as the ratio of cellular FDG events (cps) per 106 cells over the

corresponding medium FDG event density (cps/mL) (see Figure 2.3). The relative uptake ratios

of CIMR were calculated as the ratios between the normalized uptake TACs of the

corresponding culture conditions for each time point. Similarly, the relative uptake ratios for

conventional uptake experiment were computed as the ratios between uptake values at the

discrete measurement times. In this study, the uptake ratios relative to that with the culture

medium of 5 mM were calculated. All the processing was implemented using MATLAB 2012b

(The Mathworks, Inc., Natick, MA, USA, Program done by Dr. Kuangyu Shi).


33

2.1.4. Cellular pharmacokinetic modeling

Figure 2.4 Sketches of (A) the procedure of FDG uptake and (B) the corresponding cellular

pharmacokinetic modeling.

Based on the TACs obtained from the CIMR measurement, the pharmacokinetic parameters

related to GLUT and HK can be estimated. For FDG, the uptake is controlled by the GLUT and

HK phosphorylation (Figure 2.4A). A cellular two compartment model was constructed to

estimate the kinetic parameters relating to 18F-FDG transport and phosphorylation [3] (Figure

2.4B).

Given the medium event density 𝛽𝑐𝑚, the free FDG events in cell 𝛽𝑖𝑛 and the phosphorylated

FDG events 𝛽𝑝ℎ in cell can be described as following

𝑑𝛽𝑖𝑛(𝑡)

𝑑𝑡= 𝑘1𝛽𝑐𝑚(𝑡) − 𝑘2𝛽𝑖𝑛(𝑡) − 𝑘3𝛽𝑖𝑛(𝑡) + 𝑘4𝛽𝑝ℎ(𝑡)


34

𝑑𝛽𝑝ℎ(𝑡)

𝑑𝑡= 𝑘3𝛽𝑖𝑛(𝑡) − 𝑘4𝛽𝑝ℎ(𝑡)

where k1, k2, k3 and k4 are rate constants. k1 and k2 stand for the import and export rate of FDG

across the cell membrane respectively. k3 and k4 are the phosphorylation and de-phosphorylation

rates of FDG inside cell. The parameters were estimated by fitting the TACs derived from CIMR

measurements. The model fit was performed by a trust-region algorithm implemented using C++

with Intel Math Kernel Library (MKL).

A Michaelis-Menten equation was applied to interpret the relation between the estimated rate

constants (k1 and k3) and mRNA expressions of the corresponding enzyme or transporter [4]:

𝑘~𝑉𝑚𝑎𝑥

𝐾𝑚 + 𝐶𝑒𝑛𝑑𝑜

where 𝐶𝑒𝑛𝑑𝑜 is the concentration of the endogenous substrate, Km is the substrate concentration

at half maximum reaction rate, and Vmax is the maximum reaction rate, which is assumed to be

proportional to protein expression. Considering that medium glucose concentration was kept

constant before and during CIMR, the endogenous glucose concentration was assumed to be the

same as the medium glucose concentration. The FDG concentration is negligible compared to

the medium glucose concentration. For each cell line, we used fitting of the Michaelis-Menten

equation to understand if the Km rate can be the same for culturing with different glucose

concentrations. Nonlinear least square fit was applied to fit the cellular pharmacokinetic

parameter with the mRNA expressions.

Pearson correlation was applied to compare the relative uptake ratios between CIMR and

conventional uptake experiments. The paired Student’s t-test was used to further compare the

relative standard deviation (std/mean) of the two methods. A significance level of p<0.05 was

established.


35

2.2. Calibration and connections

2.2.1. Depth-dependent sensitivity correction

Figure 2.5 Sketch of depth-dependent sensitivity correction

The sensitivity of the positron detector decreases as the distance between the detector and the

layer of the medium increases because positrons interact before they reach the detector. The

depth-dependent sensitivity profile of the Timepix chip was calibrated by a depth-dependent

correction factor, which is estimated based on the Monte-Carlo simulations using Geant4 [Done

by Dr. Qian Wang], a toolkit modeling the physical processes of particles passing through matter

[5]. The simulation of the positron measurement using the Timepix chip was verified in previous

studies [1,6]. As the Figure 2.5 displays, a surface source emitting 18F positrons arbitrarily from

a box region (5 x 5 cm2) was placed on top of a plastic layer of thickness 180 µm, a mylar layer

of 6 µm and an air layer of 20 µm. The energy of the emitted positrons followed the theoretical 18F positron energy spectrum (maximum energy Emax = 633 keV). Then layers of water with

thickness of 20 µm were inserted one after each other between the surface source and the plastic

layer. Each situation was simulated for the deduction of the depth-dependent sensitivity function.

The derived depth-dependent sensitivity profile was derived as:

𝑓(𝑥) = −0.00104𝑥 + 0.4732 (𝑐𝑝𝑠/𝐵𝑞)

where x denotes the thickness of water between the source and the plastic layer. In the

microfluidic study, the medium is diluted with radioactivity. Given an investigated region of

area A and a radioactivity concentration of the medium of ρ, the counted events y of the positron

detector is a function of the thickness of the medium l,

𝑦(𝑙) = ∫ 𝑓(𝑥)𝐴𝑑𝑥𝜌𝑙

0

= −0.00052𝐴𝜌𝑙2 + 0.4732𝐴𝜌𝑙


36

The correction factor α for a thicker layer (thickness L2) to a thin layer (thickness L1) is as

following

𝛼 =𝐿2

𝐿1/(

𝑦(𝐿2)

𝑦(𝐿1)) =

(−0.00052𝐿12 + 0.4732𝐿1)𝐿2

(−0.00052𝐿22 + 0.4732𝐿2)𝐿1

The average diameter of the cell is 45.9 µm and we assume the thickness of the cell is 22.95 µm.

Thus, the correction factor α applied is 1.74. So the real radioactivity of 18F-FDG that is taken

up into the cells should be corrected by dividing the estimated radioactivity of the cells

(Radioactivity of cell chamber – predicted input radioactivity of the cell chamber) by a factor of

1.74.

2.2.2. Influence of sensitivity

The processing of data in this study does not consider the absolute radioactivity concentration.

Instead, it estimates the uptake values and the kinetic parameters using event density based on

measurements of captured positron events in the positron detector. Assuming the sensitivity of

the detector to the cell layer is θ and the medium events have already been corrected for depth-

dependent sensitivity to the cell layer (see 3.2.1). For delay and dispersion correction, the

measured medium event density in the cell chamber without cells is βcm(t) and the measured

medium events in medium chamber is βm(t).

𝛽𝑐𝑚(𝑡) = 𝛽𝑚(𝑡 − Δ𝑇) ∗1

𝜏𝑒−𝑡/𝜏

The absolute activity concentration Cm(t) in the medium chamber is

𝐶𝑚(𝑡) =𝛽𝑚(𝑡)

𝜃

The absolute activity concentration of medium in cell chamber Ccm(t) is

𝐶𝑐𝑚(𝑡) =𝛽𝑐𝑚(𝑡)

𝜃=

𝛽𝑚(𝑡 − Δ𝑇) ∗1𝜏 𝑒−𝑡/𝜏

𝜃

As θ is constant during the same measurement, we could derive

𝐶𝑐𝑚(𝑡) = 𝐶𝑚(𝑡 − Δ𝑇) ∗1

𝜏𝑒−𝑡/𝜏


37

Thus, the delay and dispersion correction are not influenced by the sensitivity and can be applied

to event density.

Given the event density of cells in cell chamber βcc(t), the normalized uptake

𝜔(𝑡) =𝛽𝑐𝑐(𝑡)

𝛽𝑐𝑚(𝑡)=

𝛽𝑐𝑐(𝑡)/𝜃

𝛽𝑐𝑚(𝑡)/𝜃=

𝐶𝑐𝑐(𝑡)

𝐶𝑐𝑚(𝑡)

where Ccc(t) is the absolute activity concentration of cells in cell chamber. It is equivalent to the

normalized uptake using absolute activity concentration.

Further, for the cellular pharmacokinetic modeling, the modeling equation can be derived [7]

𝛽𝑐𝑐(𝑡) = 𝛽𝑖𝑛(𝑡) + 𝛽𝑝ℎ(𝑡)

= 𝑎1𝑒−𝑏1𝑡 ∗ 𝛽𝑐𝑚(𝑡) + 𝑎2𝑒−𝑏2𝑡 ∗ 𝛽𝑐𝑚(𝑡)

where,

𝑎1 =𝑘1(𝑏1 − 𝑘3 − 𝑘4)

Δ

𝑎2 =𝑘1(𝑏2 − 𝑘3 − 𝑘4)

−Δ

𝑏1 =𝑘2 + 𝑘3 + 𝑘4 + Δ

2

𝑏2 =𝑘2 + 𝑘3 + 𝑘4 − Δ

2

Δ = √(𝑘2 + 𝑘3 + 𝑘4)2 − 4𝑘2𝑘4+

As the absolute activity concentration of cells in cell chamber Ccc(t)

𝐶𝑐𝑐(𝑡) = 𝛽𝑐𝑐(𝑡)/𝜃

=𝑎1𝑒−𝑏1𝑡 ∗ 𝛽𝑐𝑚(𝑡) + 𝑎2𝑒−𝑏2𝑡 ∗ 𝛽𝑐𝑚(𝑡)

𝜃

As θ is constant and

𝐶𝑐𝑚(𝑡) = 𝛽𝑐𝑚(𝑡)/𝜃

Thus, we can derive

𝐶𝑐𝑐(𝑡) = 𝑎1𝑒−𝑏1𝑡 ∗ 𝐶𝑐𝑚(𝑡) + 𝑎2𝑒−𝑏2𝑡 ∗ 𝐶𝑐𝑚(𝑡)


38

Using the event density is equivalent to using absolute radioactivity concentration in the

parameter estimation of cellular pharmacokinetic modeling.

2.3. Comparison with conventional uptake experiments

2.3.1. Relative comparison

One day before the measurements, the medium in the cell chamber was switched to a special

medium with 5mM, 2.5mM, or 0.5mM glucose concentration, respectively. The corresponding

medium was exchanged every 8 hours until the start of the measurements to sustain stable

glucose concentration. At the time of the CIMR measurements, the cells reached approximately

60%-90% confluence inside the cell chamber.

As reference, FDG uptake in cells was studied with the conventional uptake method [8] for each

of culture conditions used in the CIMR measurements. Two days before the uptake experiment,

approximately 6 × 104 cells in 300 µl suspension were inoculated into a well of a 24-well plate.

One day before the uptake experiment, the cells were incubated in a medium of one of three

glucose concentrations used in the CIMR studies. Medium was refreshed 4 times before the

uptake measurement. During the uptake measurement, 18F-FDG solution with the same glucose

concentration used in the corresponding CIMR measurements was applied to the cells for 4

different time periods (10, 20, 30 and 40 min). Then the cells were washed twice with ice-cold

PBS buffer and dissociated by trypsin/EDTA solution (Biochrom GmbH, Berlin, DE). The

radioactivity of the collected cells in each well was measured using a gamma counter (Wallac

1470 Wizard Gamma-Counter, PerkinElmer, MA, USA). Thus, three repeated measurements of

FDG uptake for incubation times of 10, 20, 30 and 40 min were obtained for each medium

condition. The number of collected cells in each well was counted using the automated cell

counter (Countess™, Invitrogen Life Technologies GmbH, Darmstadt, DE). The counted cell

numbers of the wells with the same culture condition on the same day were averaged.

2.3.2. Quantitative comparison

The in-culture way of FDG uptake measurements were performed to compare the FDG uptake

on-chip and off-chip quantitative.

The FDG uptake on the CIMR system are measured with flushing, 1) flush the tracer at 30 min

post FDG infusion (Flush30); 2) flush the tracer at 40 min post FDG infusion (Flush40). Each

profile was tested on 3 samples of SkBr3 cultured using medium with 0.5 mM glucose 1 day


39

before the experiment. The absolute uptake is compared with the conventional FDG uptake, as

well as the real time measurement data with CIMR measurement.

2.4. Modeling strategy with square-function infusion profiles

The FDG uptake in SkBr3 cells with 0.5 mM glucose medium are continuous measured on the

CIMR system. The same medium without FDG are flushed at 30 min and 40 min after FDG

medium infusion (with 3 repeat measurements). These two different square-function infusion

profiles are assessed with the kinetic modeling. The kinetics results are compared to the previous

step-function infusion profiles (without flushing).

2.5. qPCR

Cells in the same conditions as in the CIMR measurements were cultured for molecular biology

assays. Total RNA was extracted from each tumor cell line using the RNeasy Plus Mini Kit

(QIAGEN, Limburg, NL); cDNA was prepared from the total RNA isolated with the QuantiTect

Reverse Transcription Kit (QIAGEN, Limburg, NL). The sequences of primers for RT-PCR

were as follows: GLUT1 [9]: Forward: 5'-CAG GAG ATG AAG GAA GAG-3'. Reverse: 5'-

TCG TGG AGT AAT AGA AGA C-3'. HK2 [10]: Forward: 5'-CAA AGT GAC AGT GGG

TGT GG-3'. Reverse: CAA AGT GAC AGT GGG TGT GG-3'. Housekeeping gene: Forward:

5'-CAG ATG GCA AGA CAG TAG AAG -3'. Reverse: 5'- GGC AAA AAT GGA AGC AAT

GG-3'. All primers were synthesized by Eurofins Genomics. PCR were performed on Roche

Light Cycler480 Instrument I/II (Roche Applied Science, Penzberg, DE) with Light Cycler 480

SYBR Green I Master kit (Roche diagnostics). PCR was performed in a 20 µl reaction mixture

consisting of 3 µl PCR-grade water, 10 µl 2× Master Mix, 1 µl each 10× conc. forward and

reverse primers. 5 µl cDNA templates was added to run for 45 PCR cycles (95°C for 10 s, 50°C

for 20 s, and 72°C for 20 s per cycle). All PCR products were analyzed by 1.5% agarose gels

electrophoresis. The calculated number of specific transcripts was normalized to the

housekeeping genes β-actin (expressed as number of copies per µl of input cDNA) using the

LightCyclerTM480 software version 1.05.0.39 (Roche diagnostics, Penzberg, DE).


40



2. Iida H, Kanno I, Miura S, Murakami M, Takahashi K, Uemura K. Error analysis of a quantitative

cerebral blood flow measurement using H2(15)O autoradiography and positron emission tomography,

with respect to the dispersion of the input function. J Cereb Blood Flow Metab. 1986;6:536-545.

3. Sha W, Yu Z, Vu N, et al. Optimal design of a new kinetic strategy for extracting FDG transport

and uptake information in microfluidic multi-chamber cell culture chip coupled with PSAPD camera.

Ieee Nuclear Science Symposium Conference Record; 2009:3936-3942.


of local cerebral glucose utilization: theory, procedure, and normal values in the conscious and


5. Allison J, Amako K, Apostolakis J, et al. Geant4 developments and applications. IEEE Trans on

Nucl Sci. 2006;53:270-278.

6. Wang Q, Liu Z, Ziegler SI, Shi K. Enhancing spatial resolution of (18)F positron imaging with

the Timepix detector by classification of primary fired pixels using support vector machine. Phys Med

Biol. 2015;60:5261-5278.

7. Gunn RN, Gunn SR, Cunningham VJ. Positron Emission Tomography Compartmental Models.

J Cereb Blood Flow Metab. 2001;21:635-652.

8. Maschauer S, Prante O, Hoffmann M, Deichen JT, Kuwert T. Characterization of 18F-FDG

uptake in human endothelial cells in vitro. J Nucl Med. 2004;45:455-460.

9. Vaz CV, Alves MG, Marques R, et al. Androgen-responsive and nonresponsive prostate cancer

cells present a distinct glycolytic metabolism profile. Int J Biochem Cell Biol. 2012;44:2077-2084.

10. Wolf A, Agnihotri S, Micallef J, et al. Hexokinase 2 is a key mediator of aerobic glycolysis and

promotes tumor growth in human glioblastoma multiforme. J Exp Med. 2011;208:313-326.

Results for Continuously Infused Microfluidic Radioassay System

41

3. Results for Continuously Infused Microfluidic

Radioassay System

3.1. Reproducibility and stability

Figure 3.1 Plots of the mean and std of the normalized uptake curves for 6 repeated

measurements on CIMR with 3 different culturing conditions for: (A) the cell line SkBr3 (n=6);

(B) the cell line Capan-1 (n=6).

Mean and standard deviation of the estimated cellular uptake (mL / 106 cells) on CIMR system

for 6 repeated measurements are plotted (in Figure 3.1). For the same cell under the same

condition, the normalized uptake per cell followed a similar trend. The averaged relative

standard deviation of normalized uptake for SkBr3 in the 3 culture media was 18.5% (5 mM),

15.1% (2.5 mM) and 5.4% (0.5 mM) (Figure 3.1A). For Capan-1, the corresponding averaged

relative standard deviation was 8.8% (5 mM), 8.0% (2.5 mM) and 9.1% (0.5 mM) (Figure 3.1B).


42

3.2. Illustration of model fitting

Figure 3.2 Illustration of model fitting: (A) model fitting for delay and dispersion and the

predicted curve using the fit results; (B) pharmacokinetic model fitting for cellular uptake of

SkBr3 cells cultured with a medium of 0.5 mM glucose.

During the continuous infusion procedure, 1.3-4.5% (3.2±1.7%, n=3) loss of cells were observed

for Capan-1 and 0.9-3.5% (1.9±1.4%, n=3) loss of cells were observed for SkBr3. The difference

in sensitivity of the medium chamber and the cell chamber with no cells ranged from 0.3 to 1.1%

(0.6±0.3%, n=6).


43

The modeled curves including delay and dispersion could fit the measured curves well for the 6

calibration runs with no cells in the cell chamber (R2=0.9995±0.0003). One example is shown

in Figure 3.2A. The estimated delay Δ𝑇 (2.11±0.11 min) and dispersion coefficient 𝜏 (1.70±0.11

min-1) have small relative standard deviation (<7%). When the mean delay and dispersion

coefficients of the 6 curves were applied to predict the TAC in the cell chamber, the absolute

percentage error was 1.3±0.3% for the 6 runs (Figure 3.2A).

The measured data of CIMR were well fitted by the cellular two-compartment model with high

fitting quality (R2>0.9999), for both SkBr3 and Capan-1 under the investigated conditions.

Figure 3.2B shows an example of the model fitting for SkBr3 cultured using medium with

glucose concentration of 0.5 mM. The predicted medium events in the cell chamber (after

correction for delay and dispersion) are plotted as the red curve. After fitting using the cellular

two compartment model, the modeled total event curve fitted well to the measured total events

in the cell chamber.

3.3. Modeling strategy with square-function infusion profiles


44

Figure 3.3 A test of CIMR using square function infusion profile: (A) an example measurement

with flushing the tracer out at 30 min post infusion and the corresponding curve fitting; (B) an

example measurement with flushing the tracer out at 40 min post infusion and the corresponding

curve fitting; (C) compare the k1 parameters using step function (NoFlush, n=6), flushing at 30

min (Flush30, n=3), flushing at 40 min (Flush40, n=3) and the summary of Flush30 and Flush40

(Flush, n=6); (D) compare the k3 parameters using step function (NoFlush, n=6), flushing at 30

min (Flush30, n=3), flushing at 40 min (Flush40, n=3) and the summary of Flush30 and Flush40

(Flush, n=6);

Figure 3.3A and 3.3B show two examples of results of Flush30 and Flush40. Figure 3.3C and

3.3D compares the kinetic values of the new profiles with the previous step-function profile

(n=6). The bar labeled “Flush” is the average of Flush30 and Flush40. Compared with the result

of the previous step-function profile without flush, the values of k1 (1.17±0.27) are slightly

higher than that of the previous infusion profile without flush (0.99±0.26). But no significant

difference has been observed using non-paired t-test. The values of k3 using the flush profiles

(0.13±0.08) are almost the same as the ones (0.14±0.04) of the previous infusion profile, no

significance was observed. The overall variation of k3 for the square pulse function (n=6) is

slightly larger than the results of the step function (n=6).

3.4. Comparison with conventional uptake experiments

3.4.1. Relative comparison

Figure 3.4 Comparison of relative uptake ratio between CIMR uptake TACs and conventional

uptake measurements of (A) SkBr3 (n=6) and (B) Capan-1 (n=6). The mean and standard

deviation of 6 repeated measurements of each type were plotted.


45

The relative uptake ratios of CIMR and conventional uptake experiments for the two

investigated tumor cell lines were shown (in Figure 3.4). The mean and standard deviation were

plotted. Significant correlation had been found between CIMR and conventional uptake

experiments for either SkBr3 (r=0.98, p=9×10-6) as well as Capan-1 (r=0.95, p=0.0003). Overall,

the CIMR methods gained 11.3% relative standard deviation, which was significantly less

(p=0.004) than the uptake experiments of 20.8%.

3.4.2. Quantitative comparison

Figure 3.5 Comparison of uptake obtained on CIMR and conventional uptake experiment using

well counter: A) compare the estimated uptake between CIMR after flushing the tracer out at 30

and 40 min and conventional ex-culture uptake (n=3); B) compare the relative uptake of A by

normalizing each uptake to the mean values to 1 (n=3); C) compare two CIMR uptakes, 1) the

estimated real-time uptake values using continuous infusion profile, 2) uptake values after

flushing the tracer out at 30 and 40 min (n=3).

Compared with a conventional uptake experiment, the results were still not the same (Figure

3.5A). After normalizing with the mean uptake of flushing at 30 min post infusion, the CIMR

absolute uptake and the conventional uptake showed the same increase slope (Figure 3.5B). The

CIMR absolute uptake had a smaller variation than that from the conventional uptake experiment.

There was observable difference between the in-culture uptake and the conventional ex-culture


46

uptake after cold PBS flushing and trypsin. The in-culture retention values were always higher

than conventional uptake measurements. The influences of sample preparation procedure (wash

with cold PBS, dissociate attached cells with trypsin) on cellular uptake are not known and

further investigations are necessary to understand the difference between the in-culture and the

conventional uptake measurements.

Also, the real-time uptake estimation during continuous infusion with uptake estimation using

loading and flushing protocol were compared [1]. The uptake values obtained by measuring pure

cellular events after flushing were consistent with the continuously measured CIMR uptake

(Figure 3.5C). This supports that our CIMR method can reproduce the results as with the

previous in-culture method [1].

3.5. Correlation of kinetic parameters to qPCR results


47

Figure 3.6 Comparison of cellular pharmacokinetics of CIMR and qPCR for SkBr3 with culture

medium of different glucose concentration (5 mM, 2.5 mM & 0.5 mM): (A & B) plots of the

estimated cellular pharmacokinetics k1 and k3 based on CIMR data; (C & D) plots of qPCR

measurements of GLUT1 and HK2 for cells with the same culture conditions (n=6).

Figure 3.7 Comparison of cellular pharmacokinetics of CIMR and qPCR for Capan-1 with

culture medium of different glucose concentration (5 mM, 2.5 mM & 0.5 mM): (A & B) plots

of the estimated cellular pharmacokinetics k1 and k3 based on CIMR data; (C & D) plots of qPCR

measurements of GLUT1 and HK2 for cells with the same culture conditions (n=6).

The k1 of the SkBr3 cells cultured with 0.5 mM glucose concentration is approximately 75%

higher than with glucose concentration of 2.5 mM and 5 mM (Figure 3.6). This is in agreement

with the qPCR results of GLUT1 mRNA level, where the GLUT1 expression of cells with

medium of 0.5 mM glucose is around 60% higher than for 2.5 mM and 5 mM medium

concentrations. Similarly, the k3 of the cells cultured with glucose concentration of 0.5 mM is


48

higher than in the case of 2.5 mM or 5 mM. The HK2 mRNA levels of cells with medium of

glucose 0.5 mM is larger than 2.5 mM and 5 mM.

The k1 of the Capan-1 cells cultured with glucose concentration of 0.5 mM is around 25% higher

than with glucose concentration of 2.5 mM or 5 mM (Figure 3.7). However, the qPCR results

of GLUT1 expression of cells with medium of glucose of 5 mM is higher than for 2.5 mM and

0.5 mM. The k3 of the cells cultured with glucose concentration of 0.5 mM is more than 60%

higher than 2.5 mM and 5 mM. However, only less than 25% increase of the HK2 mRNA levels

of Capan-1 cells was observed when the condition of 0.5 mM glucose was compared with that

of 2.5 mM and 5 mM glucose.

For SkBr3, the estimated cellular kinetics can be fitted with the mRNA expressions using a fixed

Km with the determinant coefficient (R2=0.73 for k1 and R2=0.97 for k3). However, for Capan-1,

it is not possible to fit the estimated cellular kinetics with the corresponding mRNA levels using

a fixed Km (R2<0).


49

1. Vu NT, Yu ZT, Comin-Anduix B, et al. A beta-camera integrated with a microfluidic chip for

radioassays based on real-time imaging of glycolysis in small cell populations. J Nucl Med. 2011;52:815-

821.

Discussion for Continuously Infused Microfluidic Radioassay System

50

4. Discussion for Continuously Infused Microfluidic

Radioassay System

Similar to the previous approach Betabox (discontinuous microfluidic radioassay) system [1],

the proposed continuously infused microfluidic radioassay (CIMR) system allows in-culture

measurements. It does not require loading, unloading, and cleaning of the tracer medium in the

culture chamber. Thus, the measurements can deliver direct cellular uptake information without

introducing additional stress during the medium exchange process. In particular, the continuous

measurement of cellular uptake captures the full dynamic course of cellular uptake, enabling the

application of pharmacokinetic analysis. The results of these initial tests show that this system

can achieve reproducible cellular uptake measurements as well as stable estimations of cellular

kinetics. However, the chamber volume of the microfluidic chip in our system is much larger

than that in the Betabox system. This increased volume is necessary to obtain sufficient cell

uptake signals to distinguish the background medium events during the mixture measurements

of cell uptake and medium.

Technically, the following points are important for a successful CIMR measurement: (1) the

plane of each micro-chamber is parallel to the plane of the positron camera detector and has the

same distance, ensuring that signal sensitivity from both micro-chambers are equal; (2) each

micro-chamber of the microfluidic chip has the same physical dimension, and the volume of the

chamber is not changed during the fluid infusion; (3) no air bubbles in the tubing and the

microfluidic chip during the measurement; (4) the signal is strong enough to be detected by the

positron camera, which is related to the bottom thickness of the chip and the distance between

the microfluidic chip and the detector; (5) during the operation of liquid handling, the cell culture

chamber should always be kept with cell culture medium and shear stress that higher than the

infusion shear stress should be avoided; (6) cells are of a type with sufficient adhesiveness to

the bottom of the microfluidic chip, not being washed away during the infusion of tracer medium;

(7) the radio-nuclide is emitting particles which can be detected by the positron camera.

It is very important to monitor the cell number for the CIMR assay. However, it is not feasible

to monitor the cell number with microscopy during positron imaging, as the transmitted light

would be shielded by the positron camera. On the other hand, the image FOV of microscopy is

too small to cover all the cells in the chamber. An effort has been made by using digital

microscope system (DMS1000 from Leica), which enables reflection light detection.

Unfortunately, the cells on the bottom of the microfluidic chip were not visible with the digital

microscope system. The strategy then went to counting the cell number before and after the


51

CIMR measurements. In this study, we chose the counting method that averaged the cell number

counted on the microscopic images of 5 selected FOVs on the chip directly after the CIMR

measurement. The cells are reported to grow uniformly in the cell chamber of our chip [2,3].

The medium chamber in the CIMR system monitors the medium events in the cell chamber.

However, the measured medium events need to be corrected for delay and dispersion before the

TAC can be used to correct the measurement of the cell chamber. The effective volume of the

microfluidic chamber is 30 µL, the volume of two connectors is 56.5 µL each, and the volume

of the tubing in between is 14.1 µL. Under the experimental flow speed, the theoretical delay

between cell chamber and medium chamber is 2.09 min. The estimated delay coefficient Δ𝑇 =

2.11 min agrees with the theoretical estimation. Although the volume between the two chambers

may slightly vary due to the variability by connecting the chambers with the tubes manually, the

small relative standard deviation has little influence (< 2%) on the predicted medium events of

the cell chamber and can be ignored for high uptake signals.

The CIMR system is operated in a continuous infusion condition and the fluid shear stress on

cells is less than 0.13 dyn/cm2. In the physiological condition, the interstitial flow shear stress

on normal tissues has been demonstrated to be in the order of 0.1 dyn/cm2 or lower [4,5]. Thus

the fluid shear stress in our system is similar to that in the physiological conditions. In addition,

the low cell loss (<4.5%) during the CIMR measurements shows that this low shear stress does

not show significant influence on the adherence of the two investigated cells in the chamber.

However, for less adherent cells, even low shear stress may lead to non-negligible cell loss.

Further strategies need to be developed to compensate the influence of leached-out cells for

those less adherent cells. Otherwise, such cells are not suitable for the CIMR measurements.

The uptake values obtained in CIMR are calculated from event densities measured in the

medium chamber and cell chamber, which are not absolute activity concentrations. The

sensitivity of the system (calibration of event density to absolution activity concentration) is not

considered during the calculation. In this system, we put a mylar sheet (6 µm) between the

microfluidic chip and the detector to prevent possible leakage of fluid onto the semiconductor

detector during port exchange. The air space between the microfluidic chip and the detector may

change for different setups, leading to slightly varying absolute sensitivity. However, in this

study, we measured the medium chamber and the cell chamber simultaneously. Thus, the

sensitivity change did not affect the calculation of the uptake, nor the estimation of kinetic

parameters (proof in 3.2.3). The difference of absolute sensitivity observed between the medium

chamber and the cell chamber was less than 1.1% (0.6±0.3%), thus the same sensitivity was

assumed for both chambers.


52

Even after careful calibration and correction, the estimated uptake values were observed to be

different from the conventional uptake values obtained from well counter measurements

(chapter 3.4.2). This may be explained by the difference between in-culture measurement and

conventional ex-culture measurements. The influences of sample preparation procedure (wash

with cold PBS, dissociate attached cells with trypsin) on cellular uptake are not known and

further investigations are necessary to understand the difference between in-culture

measurement and conventional uptake measurements. Nevertheless, the significant correlations

between the relative uptake ratios (0.5 mM / 5 mM, 2.5 mM / 5 mM) of the two types of

measurements demonstrated the consistency of relative relations between each other. As many

studies investigate the relative differences under certain interventions, the systematic bias

between CIMR and conventional uptake may not change the results if all the investigations were

performed in in-culture measurements [1]. Smaller variations were achieved with the in-culture

measurement using CIMR, compared with the conventional uptake experiments. Thus, the

CIMR provides a stable way to investigate relative changes under interventions.

Furthermore, the uptake values obtained in CIMR are indirect estimations compared with the

previous in-culture microfluidic radioassay using a loading and flushing protocol [1]. By

adapting the infusion profile of CIMR with flushing the tracer, the pure event density of cells

can be measured and the calculated uptake values are consistent with the continuously measured

CIMR uptake (chapter 3.4.2). This supports the feasibility of the continuous estimation of uptake

during the infusion without flushing the tracer.

For the estimation of cellular pharmacokinetics, it is necessary to estimate the concentration

(event density) inside and outside of the cells. For the event density inside cells, the cell volume

needs to be considered. However, the real cell volume is difficult to measure and was therefore

estimated by the diameters of cells in the cell chamber. This may introduce bias for the parameter

estimation. Nevertheless, the estimated kinetic parameters are consistent with the data of the

same cancer in literature. For glucose concentration of 5 mM (corresponding to the human

condition), the fitted phosphorylation rates (k3) of this study ranged from 0.027 to 0.054 min-1

for the breast cancer cell line SkBr3. This is in agreement with the reported parameter range of

0.025 to 0.061 min-1 [6] and 0.012 to 0.078 min-1 [7] for dynamic 18F-FDG PET on human breast

cancer in literature. Similarly, the fitted k3 of this study ranged from 0.031 to 0.055 min-1 for

pancreatic cancer cell line Capan-1 for glucose concentration of 5 mM. This is also in line with

a reported k3 value 0.041 min-1 (separating the overall survival between 4 and 6 months) for 18F-

FDG PET on human pancreatic adenocarcinoma patients [8].

In the current set-up, the infusion protocol (input function) is a step function, which results in

short transient response followed by a linear TAC. This may introduce bias in the estimation.


53

Further constraints can be added by adapting the infusion protocol, such as a square pulse

function. In chapter 3.3, two different square function infusion profiles are tested for the

estimation of the kinetic parameters on SkBr3 cells cultured using 0.5 mM glucose. Comparing

the estimations using square function and step function, the values of k1 from the square function

(1.17±0.27) are slightly higher than that (0.99±0.26) of the previous step function. But no

significant difference has been observed using non-paired t-test. The values of k3 of the square

function (0.13±0.08) are almost the same as that (0.14±0.04) of the step function, no significance

was observed. The variation with the square function is slightly larger than the step function.

There might be bias in the estimation using different infusion profiles (input functions). For the

current studies, all the k4 values are nearly 0. Although the square infusion profile may improve

the estimation and reduce the bias compared with the step infusion profile, it does not bring

significant difference for the studies without obvious dephosphorylation. However, the square

function may bring significant improvement for the investigation of tracers with clear k4

clearances.

The estimated kinetic parameters of this study cannot directly be linked to underlying

physiological behavior. In this study, we employed a quantitative index, the mRNA level of

corresponding proteins measured using qPCR, for comparison. However, the expression levels

of the corresponding proteins may deviate from the mRNA expression levels [9]. The

investigated typical glucose transporter GLUT1 and typical phosphorylation enzyme HK2 may

not represent the overall function of multiple existing isoforms of GLUT and HK. In addition to

protein expressions, the activities of the transporters and enzymes also influence the behavior of

the transport and phosphorylation [10]. In contrast, the estimated kinetic parameters represent

the overall effects of protein expression and activity. Furthermore, it is not always possible to

fit the pharmacokinetic parameters with mRNA expressions using the Michaelis-Menten

equation [11] with a constant Km (failed for Capan-1). For each cell line, we tried to estimate Km

for various glucose incubation conditions. We used the model fitting to test if it is possible to

interpret the relation between the mRNA expression and kinetic parameters using a constant Km.

A change in the value of the rate constant is not expected to match the change in Vmax, especially,

when the concentration of the substrate (glucose level in the present case) was altered, unless

the value of Km is much larger than the substrate concentration. Thus, it is not expected that the

mRNA levels agree with the cellular pharmacokinetics of 18F-FDG in all the situations.

All the current CIMR measurements were performed under the condition of normal air, room

temperature (ca. 25 ºC) and room humidity, without the use of a dedicated incubator. Although

we tried to minimize the measurement time outside of the incubator, this may still introduce

stress to cells during the measurements leading to bias of the estimation of cellular uptake.

Nevertheless, all the environmental conditions were similar for the various runs performed in


54

this study. Their variations were small compared to the variabilities due to the many other factors

addressed earlier.

The CIMR system can be extended to measure positrons or electrons emitted by other tracers

using the same detector in this study [12,13]. The measured signal depends on the energy of the

emitted positron or electrons. For different tracers with the same positron emitter, i.e. 18F labeled

tracer, the proposed methods can be directly transferred. For tracers with different positron

emitters like 68Ga, 11C, some correction coefficients, such as decay correction, or depth-

dependent sensitivity correction coefficient need to be recalculated.

The pioneering microfluidic radioassay system [1,14] needs loading and unloading of the

incubating tracers for each assay. Therefore, the measurements are restricted to several

discontinuous time points and dynamic acquisition over a time course is still not possible. In

addition, the medium exchange procedure needs to compromise between the completeness of

the exchange medium and the maintenance of a stable culture environment [1,14]. Besides, the

residual of tracer medium in the cell chamber is difficult to control. Different from the

conventional uptake experiments and microfluidic radioassay systems, the CIMR system

introduces a real-time imaging method for measuring the pharmacokinetics between a tracer and

the attached cells. This is realized via simultaneously detecting the radioactivity events of a

medium chamber and a cell chamber under the constant infusion of tracer medium. A similar

method enabling the detection of the cellular retention and uptake kinetics is called ligand tracer

technology, which is developed by Bjorke et al. [15,16] since 2006 and already commercialized

now. The method measures the radioactivity of a fixed area where a rotating cell dish with an

angle consecutively delivers cells to a fixed area of the cell dish passes by. In each round of the

rotation, the cell area rotates into and out of the liquid radioactivity pool on the lower end of the

cell dish, and then goes to the detection area. The ligand tracer technology is simple and it

enables a time-resolved characterization of ligand-cell kinetics. Furthermore, a fluorescence

detection also developed with this method, broadens the application to affinity assays and

ligand-receptor interaction kinetics assays. This spin separation process enables the signals of

background and integrated signal to be measured within short consecutive times. As the

background and the integrated signal are not measured at the same time, the measurement is

time revolved but not a real-time measurement. The tracer pool in the culture dish is a fixed total

amount during each circle, the concentration is reduced as cells take up tracer in each round.

Similarly to the microfluidic radioassay, the cells in the cell dish undergo a cycle of immersion

into the liquid followed by deprivation in each rotation. Thus, cells experience perturbation

during each stimulation cycle, which is different from an in vivo situation.


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1. Vu NT, Yu ZT, Comin-Anduix B, et al. A beta-camera integrated with a microfluidic chip for

radioassays based on real-time imaging of glycolysis in small cell populations. J Nucl Med. 2011;52:815-

821.

2. Kim MJ, Lee SC, Pal S, Han E, Song JM. High-content screening of drug-induced cardiotoxicity

using quantitative single cell imaging cytometry on microfluidic device. Lab Chip. 2011;11:104-114.

3. http://ibidi.com/fileadmin/support/application_notes/AN03_Growing_cells.pdf.

4. Mitchell MJ, King MR. Computational and experimental models of cancer cell response to fluid

shear stress. Front Oncol. 2013;3.

5. Mitchell MJ, King MR. Fluid Shear Stress Sensitizes Cancer Cells to Receptor-Mediated

Apoptosis via Trimeric Death Receptors. New J Phys. 2013;15:015008.

6. Torizuka T, Zasadny KR, Recker B, Wahl RL. Untreated primary lung and breast cancers:

correlation between F-18 FDG kinetic rate constants and findings of in vitro studies. Radiology.

1998;207:767-774.

7. Tseng J, Dunnwald LK, Schubert EK, et al. 18F-FDG kinetics in locally advanced breast cancer:

correlation with tumor blood flow and changes in response to neoadjuvant chemotherapy. J Nucl Med.

2004;45:1829-1837.

8. Epelbaum R, Frenkel A, Haddad R, et al. Tumor aggressiveness and patient outcome in cancer

of the pancreas assessed by dynamic 18F-FDG PET/CT. J Nucl Med. 2013;54:12-18.

9. Shim HK, Lee WW, Park SY, Kim H, Kim SE. Relationship between FDG uptake and

expressions of glucose transporter type 1, type 3, and hexokinase-II in Reed-Sternberg cells of Hodgkin

lymphoma. Oncol Res. 2009;17:331-337.

10. Rodriguez-Enriquez S, Marin-Hernandez A, Gallardo-Perez JC, Moreno-Sanchez R. Kinetics of

transport and phosphorylation of glucose in cancer cells. J Cell Physiol. 2009;221:552-559.


of local cerebral glucose utilization: theory, procedure, and normal values in the conscious and


12. van Gastel R, Sikharulidze I, Schramm S, et al. Medipix 2 detector applied to low energy electron

microscopy. Ultramicroscopy. 2009;110:33-35.

13. Esposito M, Jakubek J, Mettivier G, Pospisil S, Russo P, Solc J. Energy sensitive Timepix silicon

detector for electron imaging. Nucl Instrum Meth A. 2011;652:458-461.

14. Wang J, Hwang K, Braas D, et al. Fast metabolic response to drug intervention through analysis

on a miniaturized, highly integrated molecular imaging system. J Nucl Med. 2013;54:1820-1824.

15. Bjorke H, Andersson K. Automated, high-resolution cellular retention and uptake studies in vitro.

Appl Radiat Isot. 2006;64:901-905.

16. Bjorke H, Andersson K. Measuring the affinity of a radioligand with its receptor using a rotating

cell dish with in situ reference area. Appl Radiat Isot. 2006;64:32-37.

http://ibidi.com/fileadmin/support/application_notes/AN03_Growing_cells.pdf

Materials and Methods for Multimodal Intravital Molecular Imaging System

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5. Materials and Methods for Multimodal Intravital

Molecular Imaging System

The development and validation of a multimodal intravital molecular imaging (MIMI) system

is the second main project of this thesis. The following chapters show preliminary studies of this

system. Part of the text is from [Kuangyu Shi, Zhen Liu, Sibylle I. Ziegler and Markus

Schwaiger. "System and Apparatus for Multimodality-Compatible High-Quality Intravital

Radionuclide Imaging." US 20140121493 A1, 2014].

The MIMI system includes three main parts: a multimodal compatible dorsal skin window

chamber, a rat with window chamber tumor model, and multimodality imaging techniques.

5.1. Multimodal compatible dorsal skin window chamber basics

Figure 5.1 Sketch of the multimodal compatible dorsal skin window chamber plate.

The main body of the multimodal compatible dorsal skin window chamber plate, as is shown in

Figure 5.1, comprises an observation window for various detectors, an assisting window to allow

the insertion of other obligatory non-imaging parts of the positron camera, four fixation pins to

allow the window chamber plate to be mounted with a window chamber assisting adapter and

connectors for imaging, a side wing to protect the dorsal skin window chamber against tilting,

fixation holes to mount fixation screws, a series of suture holes to allow the binding of sutures

with the tissues for the fixation of the transparent window chamber (the selected suture sites is

denoted in green). The window chamber is made from polyetheretherketone (PEEK), rigid but


57

flexural, and chemically inactive to a wide range of organic and inorganic chemicals and

solvents [1]. Besides, owing to the excellent high temperature resist property (the heat distortion

temperature is 152 ºC), the window chamber can be autoclaved for sterilization without

deformation.

Figure 5.2 Window chamber assisting adapter: (A) the upside of the window chamber assisting

adapter; (B) the downside of the window chamber assisting adapter.

The window chamber assisting adapter is used to assist the imaging of the rat tumor model with

the window chamber. The upside of the window chamber assisting adapter (Figure 5.2A) can be

mounted onto the window chamber, and the downside (Figure 5.2B) can be tightened with

screws.


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Figure 5.3 Photos of multimodal compatible rat window chamber tumor model: (A) a rat with

the multimodal compatible window chamber; (B & C) the MRI imaging setting; (D) the positron

imaging setting; (E) the luminance oxygen sensor imaging setting; (F) the fluorescence imaging

setting.

An example rat with the multimodal compatible window chamber tumor is shown in Figure 5.3.

The photos of different imaging modalities were recorded to show the availability of multimodal

imaging methods. Details of the methods will be addressed separately in the following sections.

5.2. Animal and tumor model

Athymic nude rats, RNU Rat Crl: NIH-Foxn1rnu (Strain code: 316, homozygous and

immunodeficient) were from Charles River. Human colon adenocarcinoma grade II cell line

HT-29 was selected for tumor transplantation on the RNU rats [2,3]. All the animal studies were

approved by the local governmental committee for animal protection and welfare

(Tierschutzbehörde, Regierung von Oberbayern, with license protocol number 18-13).


59

5.2.1. Window chamber implantation

Figure 5.4 Sketch of anatomical location for the dorsal skin window chamber implantation.

Numbers in red denote the screw holes used for fixation during surgery, the bright blue line

shows where sutures are needed, and the orientations are noted.

The multimodal compatible dorsal skin window chamber was implanted onto the RNU rat as

described previously [4-7]. A RNU rat of 5 weeks to 7 weeks age with weight of 120 g to 160 g

was selected for surgery. Briefly, each rat was anaesthetized with an intramuscular (i.m.)

injection of MMF (Midazolam/medetomidine/fentanyl with concentration in mg/ml of 5/1/0.05)

[8] with dosing according to Table 5.1. An injection dose of 1/3 of the initial volume will be

injected to prolong another 30 minutes when anesthesia time went beyond 50 minutes during

the surgery operation. The dorsal skin fold of the rat was sterilized and stretched. The hole 1 of

the window chamber plate was aligned to the position of ca. 2.5 cm behind the front limbs of

the rat, and both the hole 1 and the window area were marked. The skin layer within the window

area was cut open, and then was peeled from the body with the scissors. Afterwards, the two

layers of skin between the hole 1 were punctured with an ear punch. Then, the pair of the window

chamber plates was fixed onto the stretched skin fold via the punctured hole 1 with an 8 mm

PEEK screw (the two skin layers between the plates were typically 2 mm to 4 mm in thickness,

the fixation should not be too tight). A second PEEK screw was applied to fix the hole 2 of the

pair of the window plates without the skin layers in-between, and the width between the two

plates was the same as the first screw. Then, the skin fold between the hole 3 was punctured

with a gouge of 18G or 16G needle without a needle tip, and fixed with a third PEEK screw.


60

The same procedure was applied to the hole 5, but the hole 5 was punctured with an ear punch.

Hole 4 is optional for fixation. Afterwards, two U-shaped sutures were applied with a straight

needle onto the bottom of the window chamber (sites see Figure 5.4 marked with bright blue

line). Then, the opened skin in the window area was removed with a pair of scissors along the

edge of the window. The bleeding sites were pressed with an applicator for 2-3 minutes. An 18

× 18 mm cover glass placed onto the window and then fixed with a PEEK cover plate. The rat

was awaken via intraperitoneally (i.p.) injection of AFN (Atipamezole/ flumazenil/naloxone

with concentration in mg/ml of 5/0.1/0.4) according to the Table 5.1. Analgesia drugs were

administered following Table 5.2. The window area was checked 1 h after surgery, and the

bleeding sites were stopped with an applicator when necessary. The cover glass was cleaned

once a day, and changed every two days. All surgery instruments were autoclave sterile or one

time disposable, and a heating pad (37 ºC) was applied beneath the animal to keep the body

temperature.

Table 5.1 Anesthesia and anti-anesthesia dose

Animal weight (g) MMF (ml) AFN (ml)

100 0.07 0.25

125 0.08 0.31

150 0.1 0.37

175 0.11 0.43

200 0.13 0.49

Table 5.2 Analgesia drugs and their administration

Day Buprenorphin (s.c.) Meloxicam (s.c.)

surgery day (during surgery) 0,05mg/kg ~ 0,05ml/300g 1mg/kg ~ 0,6ml/300g

surgery day (~ 20:00 Uhr) 0,05mg/kg ~ 0,05ml/300g

Morning, 1day after surgery 0,03mg/kg ~

0,025ml/300g 0,2mg/kg ~ 0,15ml/300g

Evening, 1day after surgery

Morning, 2 days after surgery 0,2mg/kg ~ 0,15ml/300g


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Evening, 2 days after surgery

Morning, 3days after surgery 0,2mg/kg ~ 0,15ml/300g

5.2.2. Tumor transplantation

Single-cell suspension of HT29 cells was prepared for tumor transplantation. Briefly, after

trypsin digestion and centrifugation, the HT29 cells from the culture flask were suspended with

PBS twice in single-cell form. The cell suspension containing 2×106 cells in 0.05 ml was

prepared in an insulin needle or a 1 ml syringe with 30 G needle on the ice. The RNU rats with

window chamber were anesthetized with inhalation of isoflurane in oxygen flow. The isoflurane

was administered using Fluovac Anesthesia Systems (Harvard Apparatus, Cambridge, USA).

The rat was put into an isolation box with 5% isoflurane in oxygen (2 L/min) for fast anesthesia

induction. After the breath become slow and smooth, the rat was moved into inhalation mask

with 2% isoflurane in oxygen (2 L/min) for maintenance. The tumor cells were injected

subcutaneously (s.c.) into the skin area within the window. The diameter of the tumor was

measured on the day 4, and checked every two day afterwards. The tumor volume was calculated

with the following formula [9]: V= π × (a × b2)/6, where a and b were the longer and shorter

diameters of the tumor, respectively. The doubling time of the tumor was obtained by plotting

the tumor volume versus time.

5.2.3. In vitro histological staining

After imaging experiments, the rat with window chamber went under euthanasia with 0.5 ml

pentobarbital sodium (Narcoren, 16 g/100 ml, Merial GmbH, Germany) via tail vein injection.

The skin along the window area was cut and fixed with 4% para-formalin solution for 1 day,

then sunk into 70% ethanol for 1-7 days. The fixed tissue block went with paraffin embedding;

and cutting for immunohistochemistry staining. A haematoxylin-eosin (H&E) staining was

applied to check tumor growth state and tumor microenvironment. The paraffin tissue block

went under de-paraffin and rehydrate steps with sinking in xylene solution 5 minutes each 3

times, followed by an ethanol 100% (3 minutes) - ethanol 100% (3 minutes) - ethanol 95% (3

minutes) - ethanol 95% (3 minutes) - ethanol70% (3 minutes) cycle, and then rinsed with

distilled water for 5 minutes. For the staining, the tissue block was sunk in hematoxylin for 6

minutes, rinsed with tap water for 20 minutes, differentiated in acid alcohol for 1 to 3 seconds,

rinsed in tap water for 5 minutes, blued in 0.2% ammonia water for 30 seconds, rinsed in tap


62

water for 5 minutes, and counterstained in eosin for 15 seconds. In the last, the stained tissue

block went under dehydrate 4 steps: ethanol 95 % (3 minutes, discard waste each time) - ethanol

95% (3 minutes) - ethanol 100 % (3 minutes) - ethanol 100 % (3 minutes). After cleared in

xylene for 5 minutes twice, the tissue block was mounted with coverslips.

The whole window chamber slice on the stained slide was recorded using a fluorescence

microscopy (BZ-9000, KEYENCE), with a setting of a bright field, an objective 4 ×, and an

image merge mode.

5.3. MRI imaging

The RNU rat was anesthetized with 2% isoflurane in oxygen flow (2 L/min), and a tail vein

catheter was prepared in a preparation room. After 1 ml of physiological saline i.p.

administration, the rat was transferred into a 7T MR scanner room immediately. The rat was

fixed onto a specified rat MR bed with an air heating unit to keep the animal body temperature

stable. Its head was fixed into an anesthesia inhalation mask, and a breathing pad was placed

under the rat (RAPID Biomedical GmbH). A 1 Channel MiniFlex Coil (Diameter of 30 mm,

RAPID Biomedical GmbH) was placed on top of the window of the window chamber, and fixed.

A foam of 8 cm × 1.5 cm × 1.5 cm was placed on the other side of the window chamber plate to

stabilize the fixation. A set of 0.2 ml PCR tubes one filled with 1 mmol/L gadolinium-

diethylenetriamine penta-acetic acid (Gd-DTPA) solution and one filled with saline, were used

as references. The reference tubes were placed one top of the MiniFlex coil and fixed. Contrast

agent Gd-DTPA (MAGNOGRAF, 0.5 mmol/ml, Marotrast GmbH, Jena, Germany) was diluted

in 0.9% saline to a final concentration of 0.05 mmol/ml. The contrast agent solution was filled

into a 3 ml disposable syringe (B|Braun), and then further be filled into two proset paediatric

connection tubing (0.5 × 2.35 × 1500 mm, B. Braun, Melsungen, Germany) combined together.

The tubing was connected to the rat with a tail vein catheter. The whole set can be accommodated

into a mouse body gradient coil. During MR measurements, the electrocardiogram heart rate,

breathing rate and body temperature were all monitored (RAPID Biomedical GmbH). According

to the breathing rate, the isoflurane was adjusted from 0.7% to 1.5% to keep the breathing rate

around 50-70 times per minute. Imaging was obtained with a 7T preclinical MRI system (Agilent

Technologies, Santa Clara, USA). After positioning using a triplanar (tripilot) sequence, two

T1-weighted and T2-weighted MRI scans pre and post the Gd-DTPA solution administration

was acquired. A syringe pump (Pump Elite 11, Harvard Apparatus, Cambridge, USA) was

applied for the Gd-DTPA solution injection. The pump speed was set to 160 ml/hr, the injection

volume was set according to the body weight of the rat (5.0 mL/kg (i.e., 0.25 mmol/kg)) plus

0.09 ml which was a volume left in the catheter. Some parameters, such as slice number, FOV


63

etc. were the same as the localization. Other parameters were set specifically: T1 weighted

images (TR=300 ms, TE=1 ms, 192 × 192 matrix, 1 mm slice thickness, 4 cm × 4 cm FOV,

NEX = 4. Flip angle 60), T2 weighted images (TR=3000 ms, TE=35 ms, 192 × 192 matrix, 1

mm slice thickness, 4 cm × 4 cm FOV, NEX =4. Flip angle 60). After imaging, 2 ml Ringer-

Lactate solution was supplied to rat i.p. The animal was awaken afterwards, and kept on the

heating pad for 10 minutes.

5.4. Positron imaging of 18F-FDG

The RNU rat was anesthetized with 2% isoflurane in oxygen flow (2 L/min) using Fluovac

Anesthesia Systems (Harvard Apparatus, Cambridge, USA). A tail vein catheter was prepared. 18F-FDG of ca.150 MBq/kg body weight was prepared in ca. 0.5 ml saline solution and injected

into the tail vein with a bolus injection in 5 s, followed by 1 ml physiological saline flush. Then

the rat was awaken and put behind a shielding wall. The rat was anesthetized again 35 minutes

later. The window chamber plate with intact skin was fixed with window chamber assisting

adapter, and then fixed to a positron imaging stage. A Mylar sheet (6 µm) was placed on top of

the window to prevent possible leakage of tissue fluids that may defect the detector. A positron

camera was carefully mounted onto the four fixation pins (fiducial marker) on the window

chamber. A layer of tape was applied for fixation. The positron imaging was recorded 50 minutes

after the 18F-FDG injection, and 10 minutes of acquisition was performed at 1 frame/s.

A dynamic imaging protocol is listed here. To realize the dynamic positron imaging, a syringe

pump (Pump Elite 11, Harvard Apparatus) was applied for the tracer injection. Positron imaging

was recorded 15s before the pump injection, ca. 15 MBq/kg body weight was injected inside the

animal body. A 3-ml disposable syringe (B|Braun) was used to contain 0.7 ml 18F-FDG saline

solution, and the pump was set to drive 0.54 ml at a speed of 160 ml/h, in which 0.09 ml of tracer

solution left in the catheter. The positron imaging was acquired at 1 frame/s for 15 minutes. To

correctly evaluate the blood input function, a dynamic PET measurement was performed under

the same pump administration protocol. The imaging was performed on a micro-PET system

(Inveon, SIEMENS Preclinical Solutions, Erlangen, Germany). Three seconds after PET

measurement, the pump started via rat tail vein administration. Three rats were tested. Each rat

was imaged twice with a 3 days break in between each scan.

5.5. Luminance oxygen sensor imaging

The 2D oxygen map of the window area was measured with a mini-optical oxygen sensor

(VisiSens™ system, PreSens, Regensburg, Germany) plus oxygen Sensor Foils (SF-RPSu4,


64

PreSens, Regensburg, Germany). The rat with window chamber was anaesthetized with

isoflurane. The window chamber assisting adapter was placed beneath the window chamber

plate with intact skin to provide a stable detection condition. A piece of oxygen sensor foil (18

× 18 mm) was placed onto the surface of the tissue inside the window chamber, with the dull

white side contact to the tissue surface, ensuring that there was no air bubble in-between. The

VisiSens detector with a specified adapter (see Figure 5.3) was mounted onto the window

chamber. The specified adapter can isolate the surrounding light and keep the same detection

condition. Then, the room light was turned off, and the surrounding environment was kept in

dark. After initialization of the VisiSens detector, the image around the tumor was adjusted in

focus, and 10 consecutive oxygen imaging maps were recorded every 2 seconds. Afterwards, a

calibration experiment was performed in the same environment with the same detection settings.

Calibration with oxygen-free water was prepared 1-2 h before the experiment, which referred to

cal. 0. Surrounding air was referred as cal. 100, as the oxygen in the surrounding air was taken

as 100% saturation. The oxygen-free water [10] was prepared with dissolving 0.5 g of sodium

sulfite (Na2SO3, Sigma) and 25 µL of cobalt nitrate (Co(NO3)2) standard solution (ρ(Co) = 1000

mg/L; in nitric acid 0.5 mol/L, Westlab, Australia) in 50 mL water, and sealed in a 50 ml falcon

tube. Oxygen imaging maps with oxygen free water and with air were recorded, respectively.

5.6. Fluorescence imaging

The tumor vessel network was imaged with a fluorescence microscopy (Imager. M2, Zeiss,

Germany) equipped with a black/white CCD camera (AxioCam MRm, Zeiss, Germany), and an

image acquisition software (AxioVision, Zeiss, Germany). Fluorescein isothiocyanate–dextran

(FITC-dextran) with an average molecular weight of 2,000,000 (Sigma Aldrich, St. Louis, MO,

USA) was used as a dye to delineate the blood vasculature. The rat was anesthetized with MMF.

A tail vein catheter was prepared with an I.V. catheter with wings (24G, BD, Utah, USA) and

sealed with a stopper (B|Braun). The window chamber was fixed onto the window chamber

assisting adapter, further fixed onto a plastic plate, and then the whole set was fixed on the

microscopy stage with a long clamp. Once secured, all the settings were not touched to maintain

the same positron for sequential images from the microscopy. The body temperature of the rat

was maintained using two sealed gloves with 40 ºC water surrounding the rat. The objective of

1.25× magnification was applied for imaging. After focus adjustment, the field-of-view of tumor

microenvironment was found. A bright field image and a fluorescence image with GFP filter

were recorded. Then, a fluorescence imaging movie was recorded with exposure time of 50 ms

for 6 minutes, and three layers in z-direct were recorded with 1000 µm interval. The FITC-

dextran (50 mg/ml in physiological saline) was injected into the tail vein 15s after the start of

the video recording, with a bolus of 0.5 ml injected in 5s. After the movie recording, the


65

objective lens was moved away temporally. Then, a transparent plastic plate with reference

pattern (Figure 5.5) was mounted to the window chamber, and a fluorescence image was

recorded afterwards. The rat was awakened with corresponding AFN dose after supplement of

1ml of ringer lactate solution i.p.

Figure 5.5 Transparent plastic plate with reference pattern for localization of the fluorescence

field-of-view on the window chamber tissue. The blue drawing delineates the location of the

window chamber; the black circles are the mounting sites of the transparent plastic plate onto

the window chamber; the dashed line rectangles from outside to the inside denote an auxiliary

circuit for the green circle location, the opening area of the window chamber adapter, and the

FOV of the fluorescence microscopy with an objective lens of 1.25, separately; the red lines are

used for locating the imaging area of the tumor, each square has an area of 1 mm × 1 mm.

5.7. Data processing

All the primary data from different imaging modalities were converted to RAW files, which

were subsequently imported to PMOD (PMOD technologies ltd, Zürich, Switzerland) according

to the image resolution, image store size, and the digital form of image data storage. All the

imported imaging data was saved as NIfTl file (image suffix, .nii) for further data analysis in

PMOD. For the images co-registration, the MRI image was set as the default image, and all the

other modalities images were aligned with the MRI anatomy images using the fiducial markers

of the window chamber. The transformation information of the co-registration was saved. All

the co-registration data of the tumor microenvironment in window chamber was integrated for

analysis.


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1. Gaustad JV, Brurberg KG, Simonsen TG, Mollatt CS, Rofstad EK. Tumor vascularity assessed

by magnetic resonance imaging and intravital microscopy imaging. Neoplasia. 2008;10:354-362.

2. Marchal F, Tran N, Marchal S, et al. Development of an HT29 liver metastases model in nude

rats. Oncol Rep. 2005;14:1203-1207.

3. Vogel I, Shen Y, Soeth E, et al. A human carcinoma model in athymic rats reflecting solid and

disseminated colorectal metastases. Langenbecks Arch Surg. 1998;383:466-473.

4. Papenfuss HD, Gross JF, Intaglietta M, Treese FA. A transparent access chamber for the rat

dorsal skin fold. Microvasc Res. 1979;18:311-318.





7. Gregory MP, Andrew NF, Siqing S, et al. In vivo optical molecular imaging and analysis in mice

using dorsal window chamber models applied to hypoxia, vasculature and fluorescent reporters. Nat

Protoc. 2011;6:1355-1366.

8. Henke J, Baumgartner C, Roltgen I, Eberspacher E, Erhardt W. Anaesthesia with

midazolam/medetomidine/fentanyl in chinchillas (Chinchilla lanigera) compared to anaesthesia with

xylazine/ketamine and medetomidine/ketamine. J Vet Med A Physiol Pathol Clin Med. 2004;51:259-264.

9. Tomayko MM, Reynolds CP. Determination of subcutaneous tumor size in athymic (nude) mice.

Cancer Chemother Pharmacol. 1989;24:148-154.

10. Tschiersch H, Liebsch G, Stangelmayer A, Brisjuk L, Rolletschek H. Microsensors; 2011.

Results for Multimodal Intravital Molecular Imaging System

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6. Results for Multimodal Intravital Molecular Imaging

System

In the following sections, the images of the MRI imaging, positron imaging, fluorescence

imaging and in vitro histological staining were from the same rat, while the luminance oxygen

sensor image was from a different rat as the imaging modality was added in the late development

of this work. In sections 6.1 to 6.5, each imaging modality is addressed in detail to show the

feasibility of the imaging method separately. In section 6.5, the information from multimodal

imaging modalities of the same animal was integrated and compared.

6.1. MRI imaging

Figure 6.1 T1 weighted (A) and T2 weighted (B) MRI images of the window chamber.

T1 and T2 weighted MRI images of the slice number 14 on a tumor in the window chamber are

compared in Figure 6.1. From both images, the tumor can be easily distinguished from the

surrounding tissues. The tumor in the T1 weighted image was brighter compared with the

surrounding tissue, and darker in the T2 weighted image. The tumor area is 6.14 mm × 4.91 mm

in length and width, respectively. Here, the original MRI FOV was 4 cm × 4 cm, and only the


68

window chamber area is cropped. The spatial resolution of the image was 156 µm. The structure

and profile of the window chamber can be distinguished from both images.

6.2. Positron imaging

Figure 6.2 Positron imaging of the window chamber: (A) a merged image of the T2 weighted

MRI image and 18F-FDG uptake map of the positron image; (B) an 18F-FDG map imaged 50

minutes after the 18F-FDG injection, and the color bar beneath indicates color range versus

radioactivity in counts per second (cps); (C) a T2 weighted MRI image after the contrast agent

Gd-DTPA injection.

The 18F-FDG uptake map from positron imaging was precisely located onto the T2 weighted

MRI image (see Figure 6.2A) with the aid of the four fixation pins (fiducial markers) on the

window chamber. The location of the positron image is always fixed with reference to the four

fixation pins, as the positron camera is mounted onto the window chamber with four holes

around its detector. The 18F-FDG uptake of the tumor was obviously higher than the surrounding

tissue, which confirms the Warburg effect of increased glucose uptake in tumors. Not all the

tumor area depicted here has a higher 18F-FDG uptake, which is because the signal of the

positron image comes more from the surface of the tumor, and the MRI image was from a slice

not very close to the surface of the tumor. An MRI image more close to the surface of the tumor

is shown in Figure 6.3C. The anatomy of the tumor structure was much clearer after the contrast

agent injection. From the positron imaging (see Figure 6.2B), the tumor had higher 18F-FDG

uptake than the surrounding tissue. The image was an averaged map from a 10 minutes

measurement.


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Figure 6.3 Luminance oxygen sensor imaging of the window chamber, note that these images

are from a different rat: (A) a merged image of a T2 weighted MRI image and an oxygen map

of the luminance sensor image, yellow arrows denote the tumor blocks; (B) the oxygen map of

the window chamber, the color bar beneath indicates color range versus pO2 in mmHg; (C) a

H&E staining slice of the tumor and periphery tissues in the window area, which gives

confirmation information of the tumor and surrounding tissues located approximately in the

window chamber area, note that the image cannot be precisely compared with the in vivo images

here.

The luminance oxygen sensor map was precisely located on a T2 weighted MRI image of a

window chamber (see Figure 6.3A). The co-registration was accomplished with the aid of the

luminance sensor adapter that connected the window chamber with the luminance sensor. The

merged image indicates that the tumor area has lower oxygen levels compared to the surrounding

tissue. The tumor blocks depicted with yellow arrows on the T2 weighted image are darker than

the surrounding tissue. The diameter of the largest tumor block is 10.34 mm. The tumor surface

pO2 of the window chamber was measured with the luminance oxygen sensor. The oxygen

partial pressure map in mmHg is shown in Figure 6.3B. From the color bar, the area in blue

depicts lower oxygen levels while the area in yellow to red denotes higher oxygen levels. Note

that the oxygen map was scaled to show a gradient change around the tumor blocks. The centers

of the tumor blocks have the lowest oxygen levels. The histological staining of the tissue in the

window chamber area is shown in the Figure 6.3C, confirming the tumor blocks’ locations.


70

Figure 6.4 Luminance oxygen sensor image and its pixel-pO2 histogram. The color bar indicates

color range versus pO2.

The luminance oxygen sensor image is shown in Figure 6.4, along with its pixel-pO2 histogram.

The pO2 frequency of the whole oxygen map is shown in the histogram, which was drawn using

a software called VisiSens AnalytiCal 1 from the oxygen sensor company, and the unit of the

pO2 was converted from the [%] air saturation into the mmHg. The tumor areas in the oxygen

map have an average of 11.67 mmHg, a maximum of 20.99 mmHg and a minimum of 4.67

mmHg. The necrosis area has an average of 7.46 mmHg, a maximum of 10.73 mmHg and a

minimum of 4.67 mmHg. The whole FOV of the oxygen map has an average of 14.62 mmHg,

maximum of 39.19 mmHg, and a minimum of 4.67 mmHg. So the surrounding tissue has an

average of 17.07 mmHg.


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Figure 6.5 Fluorescence imaging of the window chamber: (A) a merged image of the T2

weighted MRI image and a fluorescence image with FITC-dextran (mw: 2e6, 250mg/kg); (B) a

fluorescence FITC-Dextran image of the window chamber to visualize the blood vasculature

around the tumor tissue. The color bar indicates color range versus fluorescence intensity.

The location of the FITC-dextran fluorescence image is shown in the T2 weighted MRI image

of the window chamber (see Figure 6.5A). The precise co-registration was accomplished with

the aid of the transparent plastic plate with reference pattern (refer to Figure 5.6). From the

merged image, the microvessels around the tumor are non-homogenous. Some areas were highly

vascularized, some areas in adjacent were poorly vascularized, and more blood vessels were

condensed around the tumor edges. The FITC-dextran fluorescence image of the FOV is shown

in Figure 6.5B. The fluorescence intensity was condensed in the larger blood vessels. The

resolution of the fluorescence image was 5.16 µm.


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6.5. In vitro histological staining

Figure 6.6 H&E staining of the tissue sample taken from the window chamber area after the

sacrifice of the rat. Yellow arrows depict the tumor locations in the tissue.

The H&E staining of the tissue in the window chamber is shown in Figure 6.6. The whole tissue

section was recorded by combing each microscopy FOV of the tissue section. The areas

condensed with purple color depict the tumor tissues. From the H&E staining image, three tumor

blocks can be visualized. The biggest tumor block is 6.11 mm × 4.84 mm in length and width,

and the two smaller ones with diameters of about 0.7-0.8 mm.


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6.6. Integrated tumor microenvironment imaging

Figure 6.7 Multimodal intravital imaging of a tumor microenvironment within the window

chamber: (A) a T1 weighted MR image, (B) a T2 weighted MR image, (C) a FITC-Dextran

fluorescence image, (D) an 18F-FDG positron image, the locations of the images were delineated

with the red boxes linked with red arrows separately; and (E) a H&E staining slice of the tumor

and periphery tissue. Note that the H&E image is an ex vivo image, only gives confirmation of

the location of the tumor and surrounding tissue, and cannot be precisely compared with the in

vivo images listed here.

Multimodal intravital imaging of a tumor in the window chamber was integrated to provide

various information about the tumor microenvironment (see Figure 6.7). The tumor tissue was

darker than the surrounding tissue in the T2 weighted map, whereas whiter in the T1 weighted

map. The H&E staining image confirmed the location and shape of the tumor. The FITC-dextran

fluorescence image of a smaller FOV inside the tumor showed a detailed blood vessel network

in micrometer range. The complexity of the tumor network shown in the fluorescence was

consistent with the MRI observation. The positron imaging map showed that the 18F-FDG is

accumulated and trapped inside the tumor area, which is consistent with the Warburg effect.

Discussion for Multimodal Intravital Molecular Imaging System

74

7. Discussion for Multimodal Intravital Molecular Imaging

System

As the preliminary study with the MIMI system is in an establishment stage, more details of the

discussion will be focused on the methods and techniques of the system.

7.1. Implantation with multimodal compatible window chamber

The design of the final version of the multimodal compatible window chamber went through

two versions’ update during development, and the second version was patented [1]. The second

version of the window chamber has minimal invasiveness to the rat as the fixation of the window

chamber plates do not penetrate the tissues, however, the clamp way of the fixation design is not

as friendly as expected to the animal. The rat with the window chamber showed unbearable

behaviors two or three weeks after the implantation, such as bites and clawing, trying to get rid

of the window chamber. This is because the tight fixation hinders the blood flow of the tissues

in between, especially when the animal grows larger and furs grow thicker. Besides, the extruded

fixation part of the window chamber sometimes hinders the animal from free moving, and the

animal may be stuck into a food shelf in the cage. Thus, the window chamber has position shifts

over time, and could not attain the purpose of accurate co-registration of images obtained over

days and weeks. The latest version fixed this problem with a fixation using three screws

penetrating the rat skin tissue, and the stability is much improved. Furthermore, the dimension

is reduced. The rat carrying the window chamber can move freely in the cage. Thus, the window

chamber showed better ergonomics.

The suture’s position and tightness of the window chamber are the key for a successful

implantation. Although there are many suture sites designed on the window chamber, not all the

suture sites are required. If all the suture sites are sewed, the blood flow of the tissue inside the

window will be hindered, leading to edema around the window area and tissue inflammation

and necrosis. Thus, tumor cells cannot be successfully transplanted. As noted in chapter 5.2.1,

only the sites at the bottom of the window are required to be sewed to avoid the bottom skin

being stretched away from the window area. And the suture is not supposed to be very tight to

ensure a well perfused blood flow.

The window of the chamber is designed with a rectangular shape, instead of the conventional

round shapes [2]. This is necessary to fix the positron camera for a close contact imaging position.

The window chamber can be mounted onto the positron camera, a mini optical sensor and


75

microscopes with the fixation pins (fiducial marker) and the window chamber assisting adapter.

The physical fiducial marker allows direct and precise co-registration of images from the above

mentioned imaging modalities plus MRI and PET.

7.2. Animal tumor model

There is an alternative way to perform the window chamber implantation and tumor

transplantation in compare to literatures [3-6]. Normally, window chamber is implanted first and

after 3 days, a tumor is transplanted either with a tumor tissue block or tumor cells suspension.

Alternatively, the sequence can be performed reversely. The tumor cells suspension is first

injected in the dorsal skin layer of the rat, and the injection site is where the window of the

window chamber will be placed. In this way, not only the time for a rat bearing the window

chamber is shortened, but also a highly successful rate of the tumor transplantation is ensured.

For the HT-29 tumor cell line, the tumor is then measured from the day 4, when the tumor is

visible. The window chamber is implanted when the tumor grows to the required size. During

the operation, the skin layer without tumor will be cut and excluded, the tumor tissue inside the

window chamber should kept as intact as possible, and the blood vessels in the fascia should be

carefully preserved. In the early time after surgery, regular checking to stop bleeding and

cleaning of the screening liquids are necessary.

7.3. MRI imaging

The preclinical MRI imaging has a spatial resolution of around 100 µm. Most of the anatomical

structures of the tumor and surrounding tissue can be clearly distinguished, as shown in the T1

and T2 weighted images. This is important for the multimodal imaging, because the fiducial

markers on the window chamber can be precisely located. Furthermore, the MRI images have

the largest FOV (4 cm × 4 cm) compared with other imaging modalities applied, and all the

information of the window chamber can be collected up. In addition, the MRI imaging is a 3D

imaging technique, and the MRI slice in the Z direction can be located. Thus, T1 and T2

weighted images are set as the default images for the image co-registration.

7.4. Positron imaging

Positron imaging is mainly based on contact imaging. However, the tumor inside the window is

not a real flat plane as tumor grows in 3D direction. Thus, the contact between the tumor surface

and the detector plane of the positron camera could be uneven, especially when the tumor


76

volume grows larger. This is a challenge for the detection as the positron imaging is very

sensitive to the imaging distance [7]. The closer the tissue approaches to the detector of the

positron camera, the more enhanced position signal is detected. Besides, the breathing of the

animal may lead to a variation in the distance between the positron camera and the window

chamber. Thus, it is crucial to have a fixation setting to ensure an even and stable contact

between the tissue plane and the positron camera. Here, the window chamber and the window

chamber assisting plate was mounted onto a fixation stage, which reduces the vibration from the

animal breathing. And to avoid the liquids screened from the tissue destroying the positron

camera, it is suggested to place the positron camera on top of the window chamber. In further, a

layer of Mylar foil (6-µm-thick) was placed in-between to protect the positron camera. The

tumor uptake of the 18F-FDG was visible with exposures of 8 seconds, which is consistent with

the results reported by Zhonglin Liu et al. [8]. However, in the averaged image (the cps map) of

the 10 minutes’ measurements, the microvessels of the tumor are still not distinguishable.

In the method description, a dynamic positron imaging protocol is proposed, which is useful for

kinetic modeling analysis to elucidate the underlying pharmacokinetics. However, it is very hard

to extract the blood input function (the time course radioactivity of the tracer in blood) of the

dynamic positron imaging since the arterioles cannot be distinguished from the positron images.

Hence, the quantitative analysis and kinetic modeling are not applicable yet, unless the blood

input function of the tracer can be captured. One possible solution is to perform a dynamic PET

imaging, where the heart or aorta of the rat is used to access the blood input function. By using

the same administration program with a syringe pump, the blood input function is transferable

from the dynamic PET into the dynamic positron imaging approximately. In total, an average of

six measurements should be enough for the assessment. The second way to measure the blood

input function of the animal can be performed with the aid of a tubing that connecting the

femoral artery of the rat with the femoral vein, where the radioactivity of the circulation blood

can be measured with a detect machine. However, there are a lot of technological and animal

welfare issues to be solved for this method. A simple and maybe effective solution is to perform

the positron imaging and PET measurement simultaneously, using the same pump

administration protocol. In this way, only a single tracer injection is needed, and both, the

positron imaging of the window chamber, and the PET imaging focusing on the heart can be

performed. Unfortunately, this is not yet feasible with the current setting of the window chamber

and the size of the positron camera, which cannot be accommodated into the field-of-view of a

preclinical PET scanner. Furthermore, the camera located inside the PET scanner may hinder

part of the signal acquisition of the rat, introducing a problem for the image reconstruction of

the PET data.


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The oxygen sensor foil can be used either with or without peeling off the protection layer. From

the technical note of the company, the imaging resolution is increased from 100 µm to 25 µm

when the protection layer is peeled off. However, when the protection layer is kept, the

reproducibility of the measurement was better from our test experiments. This is because the

sensitivity of the measurement was much increased along with the increased imaging resolution.

Since the tumor and the surrounding tissue inside the window area is not an absolute flat plane,

the increased sensitivity may introduce more noise than the real signal, especially when the

tumor grows larger in a 3D direction way. Thus, the protection layer of the oxygen sensor foil

was kept during the measurements.

The pO2 value of the luminance oxygen sensor image is determined with the calibration of the

surrounding oxygen levels in air and oxygen free values. So the detection and calibration should

be performed under the same environment and with the same detection setting. Although the

adapter of the luminance oxygen sensor shadows the surrounding light away, it is still important

to avoid the daylight or room light during the calibration and measurement, ensuring a stable

measurement conditions all the time. Before each measurement, it is necessary to initiate the

oxygen sensor for an auto adjustment of the sensor itself.

The oxygen partial pressure of the air is considered to be 160 mmHg at the sea level and at room

temperature, which equals to 21.1% of the oxygen in the air [9]. Here, the partial pressure in

Munich is considered to be 155.5 mmHg for 21.1% oxygen in the air, which is a converter factor

suggested from the sensor company. The tumor has an average of 11.67 mmHg pO2 value

measured from the oxygen map. The critical pO2 in tumors is proposed to be 8-10 mmHg [10].

However, there is strong inter-tumor variability of the oxygenation pattern from different tumor

samples. The averaged mean pO2 was ranging from 13 to 59 mmHg in a breast cancer study

measured with an oxygen electrode method [11], and the median pO2 was ranging from 0 to 54

mmHg with 28 tumor samples measurements [15]. Apart from an absolute comparison of tumor

pO2 values, pO2 value is always lower in the tumor than in the respective normal tissue, which

is the true definition of hypoxia [12]. Here, the pO2 of the surrounding tissue has an average of

17.07 mmHg, 5.4 mmHg higher than the tumor tissue. The tumor necrosis area that was proved

from the HE staining shows an averaged pO2 value of 7.46 mmHg, and the lowest value of 4.67

mmHg, which is consistent with the observation that the pO2 value lower than 8 mmHg has

detrimental changes of tumors (ATP depletion, intracellular acidosis, apoptosis…) [12].

The tumor measured in a window chamber with the oxygen sensor is a special situation, since

the tumor is in the sub-cutis layer of the skinfold on the rat. In a study of human skin pO2


78

measurement with microelectrodes, the pO2 increased with the increase of the depth of the skin,

the pO2 was 8.0 ± 3.2 mmHg in the superficial region of the skin (5-10 µm), the pO2 was 24.0 ±

6.4 mmHg in dermal papillae (45-65 µm), and the pO2 was 35.2 ± 8.0 mmHg at the surface of

the sub-papillary plexus (100-120 µm) [9]. So the way of contact imaging of the skin layers may

also affect the measured pO2 values, which is confirmed by pioneer studies with a rat window

chamber [13]. They proved that there are significant differences of the pO2 values between

fascial and tumor surfaces [13]. The tumor pO2 map was measured with phosphorescence

lifetime imaging using a Pd-meso-tetra-(4-carboxyphenyl)-porphyrin [13-15]. The luminance

oxygen sensor foil applied in our study is also a similar porphyrin product, however, has a

contact on the surface of the tumor tissue. As the fascia layer is closely connected with the tumor

surface, the measured signal is a combination of the fascia layer and the tumor surface. Our

averaged pO2 data cannot be directly compared to the median pO2 values presented in those

studies, however, the pO2 range in the studies is broader than ours [13,15]. This is in consistent

to the decreased sensitivity in the measurement without peeling off the protection layer of the

oxygen sensor foil.


Fluorescence imaging has the highest resolution but the smallest imaging field-of-view

compared to other imaging modalities, introducing a challenge for the images co-registration.

The coordinate of the microscopic image cannot be precisely located only with the fiducial

markers of the window chamber. Here, the introduction of the transparent plastic plate solved

this problem. It has a connection with both microscopy imaging and fiducial markers of the

window chamber, so that the field-of-view of the microscopy is orientable in the window

chamber. Furthermore, the microscopically differentiable pattern of the transparent plastic plate

supplies reference of the length and orientation, assisting the localization of the tumor in the

window chamber. The stable fixation of the window chamber onto the microscopy was

accomplished with the aid of the window chamber assisting adapter, a plastic base and a large

clamp. The whole setting also ensured the skin fold tissue inside the window chamber to be

parallel to the lens of the microscopy, avoiding a blurred image.

7.7. Multi-modality imaging

The multi-modal imaging gives various information about the same tumor microenvironment,

however, there are biological and technical issues to be considered. First, the animal goes under

anesthesia in every single imaging modality measurement. It takes 10 minutes for the luminesce


79

oxygen sensor imaging, 1h for the MR imaging, 20 minutes for the positron imaging, and 45

minutes for the fluorescence imaging. The total anesthesia time will be a burden for the animal

if these four or more imaging modalities are performed together. Spreading the imaging into

different days gives the animal certain recovery time, but in contradiction, the images from

different days or weeks may not be comparable. Thus, it is important to appropriately arrange

the imaging time and sequences. Here we propose an integrated imaging protocol in

consideration of both animal welfare and the effectiveness of each imaging modality for

comparison. The MRI imaging is scheduled first and will take 45 to 60 minutes with isoflurane

anesthesia. In following, the luminance oxygen sensor imaging is scheduled immediately, which

takes 10 minutes. Afterwards, the animal will be waken up for resting and recovery for about 2

hours, in a cage with heating pad underneath and an oxygen flow supply. Then, the rat is

anesthetized with MMF to perform fluorescence imaging which costs about 45 minutes,

followed by 1-hour recovery. Last, the positron imaging is set with isoflurane anesthesia. The

rat will go under 10 minutes’ anesthesia for the tracer injection, then awake for 40 minutes for

resting, and then 10 minutes’ anesthesia for the positron imaging acquisition. Afterwards, if the

rat is sacrificed, the tissue inside the window chamber will be cut and kept into a 4% para-

formalin solution for further immunohistochemistry operations. Second, the orientation and

coordinate for image co-registration have to be considered. The application of several tools, such

as the window chamber assisting plate, luminance sensor adapter, and the transparent plastic

plate, aided the co-registration in different aspects. The details were discussed separately in each

section. Some pattern with orientation information can be added onto the next generation

window chamber, making it easier to find the anatomical orientation among different images

from the multimodality imaging.


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1253.

13. Erickson K, Braun RD, Yu D, et al. Effect of Longitudinal Oxygen Gradients on Effectiveness

of Manipulation of Tumor Oxygenation. Cancer Res. 2003;63:4705-4712.

14. Dewhirst MW, Ong ET, Braun RD, et al. Quantification of longitudinal tissue pO2 gradients in

window chamber tumours: impact on tumour hypoxia. Brit J Cancer. 1999;79:1717-1722.

15. Cárdenas-Navia LI, Mace D, Richardson RA, Wilson DF, Shan S, Dewhirst MW. The Pervasive

Presence of Fluctuating Oxygenation in Tumors. Cancer Res. 2008;68:5812-5819.

Summary

81

8. Summary

Molecular imaging has been extensively used to investigate tumor metabolism and the tumor

microenvironment. However, systematic linking of the imaging with the tumor physiology is

still limited, due to the complexity of tumor metabolism and the heterogeneity of the tumor

microenvironment. This thesis makes the following contributions in new methodological

developments: (I) a novel imaging method and system (CIMR system) is developed for tumor

cells’ metabolism in vitro, which can be extended to investigate the interactions between a

radiolabeled molecule and the cells; (II) a novel multimodal intravital molecular imaging system

and method (MIMI system) is developed for measuring physiological features of tumor

microenvironment in vivo.

The CIMR system provides an innovative imaging method that imaging in a fluids infusion

condition, in which a medium chamber and a cell chamber were imaged simultaneously to

extract the real-time uptake of the radioactive tracer for adherent cells. Cellular pharmacokinetic

modeling can be achieved based on the dynamic measurements. The estimated cellular kinetic

parameters were verified by comparing with the mRNA expression levels. In particular, the

developed CIMR system has the following methodological contributions:

For the first time, an in-culture continuous measurement system was developed to online

imaging the uptake signals of radioactive tracer with minimum disruption and easy

operation

A high-resolution positron camera based on single-particle counting silicon pixel

detector Timepix was proposed to obtain high-quality measurements of radioactive

signals.

A method was developed to distinguish the medium signal and the cellular uptake signal

via simultaneously recording an additional reference medium chamber during infusion.

A cellular compartmental model was introduced to estimate the underlying

pharmacokinetics.

A mathematical model was developed to correct the delay and dispersion of radio

activities between the medium chamber and cell chamber.

A depth-dependent sensitivity correction method was developed to compensate the

influence of distance to the detector on the imaging sensitivities.

A cell counting method for microfluidic chip was selected after exploring different

strategies to achieve a robust and precise measurement of small cell population (~104)

with smallest variation and highest precision.

Summary

82

The initial experiments demonstrated the reproducibility, stability and feasibility of capturing

pharmacokinetic differences of the CIMR system. It provides a platform for convenient

quantitative investigation of cellular physiology and pharmacokinetics.

The easy and robust in-culture real-time measurements of the developed CIMR system provides

the potential for several applications for future work:

Several other tracers and cell lines may be investigated. For example, the clearance of

amino acid tracer 18F-fluoroethyl-L-tyrosine (18F-FET) in malignant glioma cells can be

investigated in-depth by the system.

The CIMR system may be further extended by integrated with other monitoring methods

such as florescence microscopy or luminescence sensor imaging.

The real-time monitoring of cellular changes with drug interventions may be achieved

by advanced modeling on the online measurement data.

More complex topology of fluid paths and chambers can be extended to simulate vessel

network and organ interactions to investigate advanced pharmacokinetics and

pharmacodynamics.

The MIMI system provides an imaging tool combining four imaging modalities (positron

imaging, MRI, fluorescence imaging and luminescence sensor imaging). The accurate co-

registration of images among the imaging modalities ensures a comprehensive understanding of

the tumor microenvironment. This system can bridge the discrepancies between macroscopic

and microscopic images and between in vivo and in vitro images and provides a tool for the

regional investigation and longitudinal observation of underlying physiology within an intact

tumor tissue. Several methodological contributions have been made during the development of

this system:

A special dorsal skin chamber was designed to provide a dedicated window for the

investigated multimodal imaging.

A tumor model was investigated and prepared for intravital investigation.

Special biocompatible materials for window chamber was investigated and selected for

robust and multimodal compatible imaging.

A particular fiducial coordination system was developed to allow precise physical co-

registration among different imaging modalities.

A dedicated positron imaging protocol was established for Timepix detector.

The preliminary test results demonstrated that the dorsal skin window chamber tumor model

was feasible for multimodal intravital imaging. The multimodal imaging provided rich

Summary

83

information about the tumor microenvironment, which may be useful to investigate the

physiological principles of molecular imaging as well as efficient drug screening with in-depth

knowledge of underlying changes of tumor microenvironment.

List of Abbreviations

84


18F-FDG 2-Deoxy-2-(18F)fluoro-D-glucose

18F-FET 18F-fluoroethyl-L-tyrosine

AFN Atipamezole/ flumazenil/naloxone

AIF Arterial input function

ATP Adenosine 5 -́triphosphate

BLI Bioluminescence imaging

BST Blood supply time

CCD Charged-coupled detector

CIMR Continuously infused microfluidic radioassay

CLSM Confocal laser scanning microscopy

CMOS) Complementary metal oxide semiconductor

Co(NO3)2 Cobalt nitrate

cps Counts per second

CT Computed tomography

DCE-MRI Dynamic contrast-enhanced magnetic resonance imaging

DNA Deoxyribonucleic acid

dNTPs Deoxynucleoside triphosphates

EOF Electro-osmotic flow

FITC-dextran Fluorescein isothiocyanate–dextran

FMT Fluorescence molecular tomography


85

FOV Field-of-view

FRI Fluorescence reflectance imaging

Gd-DTPA Gadolinium-diethylenetriamine penta-acetic acid

GFP Green fluorescent protein

GLUT1 Glucose transporter-1

GLUTs Glucose transporters

H&E Haematoxylin-eosin

HIF-1 Hypoxia induced factor -1

HK2 Hexokinase-II

HKs Hexokinase

IVM Intravital microscopy

LDH Lactate dehydrogenase

MCT Monocarboxylate cotransporter

MEMS Microelectromechanical systems

MIMI Multimodal intravital molecular imaging

MKL Math Kernel Library

MMF Midazolam/medetomidine/fentanyl

MP Multiphoton

MR Magnetic resonance

MRA Magnetic resonance angiography

MRI Magnetic resonance imaging


86

mRNA Messenger ribonucleic acid

MRS Magnetic resonance spectroscopy

mw Molecular weight

Na2SO3 Sodium sulfite

NEX Number of excitation

NIR Near-infrared

OFDI Optical frequency domain imaging

PCR Polymerase chain reaction

PD Pharmacodynamics

PDMS Polydimethylsiloxane

PEEK Polyetheretherketone

PET Positron emission tomography

p.i. post injection

PK Pharmacokinetics

PLI Phosphorescence life time imaging

pO2 Oxygen partial pressure

PSAPD Position sensitive avalanche photodiode

qPCR Quantitative real-time polymerase chain reaction

RNA Ribonucleic acid

s.c. Subcutaneously

SPECT Single photon emission computed tomography


87

TACs Time activity curves

TRICKS Time-resolved imaging of contrast kinetics

μ-TASs Micro total analytical systems

Acknowledgements

88

Acknowledgements

I would like to thank my supervisor Prof. Dr. Sibylle Ziegler for giving me the opportunity to

do my PhD research in her lab and always support for my PhD project. She always tries to make

time to solve my problems no matter how busy she is. Whenever I have difficulties, she is always

by my side. She encourages me to face the difficulties of the project and forgives me of the

mistakes I made during the studies. At the same time, her conscientious attitude to the scientific

research sets a good example, which leads me to do the research strictly. The rigorous as well

as relaxed research atmosphere in her lab allows me to finish my study in a pleasant way. Prof.

Dr. Ziegler also fully understands the difficulties I may encounter in daily lives as a foreigner in

Germany, and she is always willing to offer a hand, these feelings of warmth will surely become

my precious memories for years to come.

I would like to thank for my tutor Dr. Kuangyu Shi’s guidance, and I am really appreciated to

his support from the provision of software to hardware. Dr. Shi directs me in almost every aspect

of the PhD studies. Whenever I have problems in techniques or suffer from bad experimental

results, I would turn to him for help, and he would kindly solve them with me together through

countless of tries and tests. This thesis would definitely be impossible if without his efforts and

dedication. Besides, he also offers me many useful suggestions in the daily life issues. I would

like to express my sincere thankfulness to you for all the kindness in both academic and daily

routine.

I would like to thank my thesis committee mentors Prof. Dr. Gabriele Multhoff, Prof. Dr. Markus

Essler and Prof. Dr. Gil Westmeyer. They provided me with a lot of valuable suggestions, the

friendly and relaxed communication are impressive to me and encourages me to fulfill my study.

I would like to thank Prof. Dr. Markus Schwaiger. He is a great leader of prospective vision.

Without his support for the devices and other important investments, the PhD project would not

happen or proceed so smoothly. His critical viewpoints lead a clear way to the constant upgrade

of the systems we are developing. Prof. Dr. Schwaiger allows researchers to try and error in

various new technologies and fresh research ideas. And he is lenient to me for the mistakes I

made during the exploration of new studies until I made some steps forward. I really appreciate.

I would like to thank Prof. Sung-Cheng (Henry) Huang for his guide and help regarding to the

kinetic modeling and the microfluidic system studies in both TUM and UCLA. His

conscientious and modest attitude is highly admirable. I would also like to thank Dr. Koon-Pong

Wong for the assistance and help for my short time study in UCLA.

Acknowledgements

89

I would like to thank co-workers Dr. Qian Wang, Tao Cheng, Ziying Jian for the experiments

and data analysis supports. I would like to mention that Tao Cheng and Ziying Jian also helped

the second sub-project especially about the optimization of the luminance oxygen sensor

imaging protocol and the images co-registration. I would like to thank Dr. Christof Seidl and Dr.

Benedikt Feuerecker for helping me translate my English abstract into the German version.

I would like to thank Dr. Geoffrey Topping and Dr. Franz Schilling for setting the MRI

acquisition parameters, testing and establishing most of the MRI protocols with me. I would like

to thank Stephan Düwel, Christian Hundshammer, Eugen Kubala and Dr. Giorgio Pariani for

helping me exploring some MRI imaging protocols and doing some MRI experiment with me.

I would like to thank you all for the guide of the MRI knowledge and many useful discussions

with me. I would like to thank Aayush Gupta for helping me with some MRI measurements. I

would like to thank our MRI technician Michael Michalik for helping of some MRI experiments’

preparation.

I would like to sincerely thank Sibylle Reder and Markus Mittelhaeuser for helping of PET and

positron measurement. Without their supports and help, it is not possible for me to finish my

experiments. I would like to thank Michael Herz for supporting and help of tracer production

and radio analysis techniques. I would like to thank Reinhold Klitfch and Marina Schenk for

supporting and help of tracer production.

I really appreciate all the help Dr. Liang Song and Dr. Detian Yuan gave when I was searching

for a reliable cell counting method. It is Dr. Song's suggestion that led us to find the best method

for assessing cell numbers on a chip with a small population.

I would like to thank Dr. Sabine Schwamberger, Dr. Anne-Kathrin v. Thaden, Susanne Swirczek

and Miriam Mohring for helping animal experiments and taking care of the animals. I would

like to thank workers from Klinikaustauschraum and ZPF. I would like to thank Dr. Katja Steiger

for supporting of pathology check for the stained slides.

I would like to thank for Dr. Ian Somlai and Andreas Seiler for helping fabricate of imaging

platform. I would like to thank Dr. Iina Laitinen, Birgit Blechert, Birgit Meißner, Christine

Koppenhöhl, Katja Steiger, Sabine Pirsig, Manja Thorwirth for their help and support.

I would like to thank Dr. Armin Bieser (iBidi GmbH) for the support and help all aspects of

knowledge about the ibidi chips and Dr. Jan Tous (CRYTUR, spol. s r.o.) for supports related

to the positron camera.

I would like to thank Dr. Stefan Stangl, Dr. Christine Bayer for helping of immunofluorescence

staining and fluorescence microscopy. I would like to thank Aayush Gupta and Franziska Hanus

Acknowledgements

90

for guide of BCA protein assay, and helpful discussion with Dr. Benedikt Feuerecker and Nahid

Yusufi.

I would like to thank Sybille Reder, Dr. Christof Seidl, Dr. Franz Schilling for helping me handle

some daily lives problem in Germany. I would like to thank Dr. Christof Seidl for many helpful

discussions.

I would like to express respects to Sibylle Reder and Michael Herz, they are really diligent

colleagues in the Nuclear Medicine. They have done excellent jobs, they take care of every

details and people can fully trust the work they do. They showcase the genuine German

technician and radiochemist spirits.

I would like to thank the former Consul Jiqiang Dai and Consul Prof. Dr. Chongling Huang from

Chinese Consulate-General in Munich. They offered me a lot of help and opportunities during

my stay in Germany.

I would like to thank Dr. Katrin Offe and Desislava Zlatanova for their support and help, they

did a great job in coordinating our academic activities, and they always answer my questions

and solve my problems quickly and sufficiently.

I would like to thank all of my colleagues: Dr. Xiaoyin Cheng, Dr. Qian Wang, Dr. Florian

Schneider, Dr. Ian Somlai, Tao Cheng, Ziying Jian, Lina Xu, Sasa Cheng for the discussion and

help in both academic and daily life.

I would like to thank all my friends in Germany. Dr. Yu Wang, Dr. Jun Zhao, Dr. Shenghan

Wang, Yamin Zhao, Baocai, Yin Li, Dr. Xin Bian, Shaoxia Jin, Jialin Yen, Rong Li, Haifeng

Yu, Ying Ouyang, Lei Bao, Simin Hu, Jiachen Shen, Chengxi Lin, Christian Agsteiner, Tobias

Rmk Meyer, Fabian Franke, Alex Bazhenov, Beni, Ferdinand Rupp, Hazel Han, Marcos Falcon,

Martin Sälzle, Dr. Joachim Pircher, Dr. Maxim Barenboim, Dr. Xiaoyin Cheng, Dr. Qian Wang,

Dr. Wentao Song, Dr. Florian Schneider, Dr. Ian Somlai, Tao Cheng, Ziying Jian, Xiaopeng Ma,

Lina Xu, Sasa Cheng, Gang Yuan, Dr. Liang Song, Dr. Bo Kong, Prof. Dr. Gang Zhao, Dr.

Xiaoling Liang, Dr. Zhoulei Li, Dr. Yuchen Xia, Dr. Xiaoming Cheng, Dr. Yun Zhang, Dr.

Lianpan Dai, Dr. Detian Yuan, Dr. Chenyu Zou, Dr. Meng Chun, Dr. Shanshan Luo, Dr. Jingjing

Xia, Lunda Gu, Miao Lu, Jiaoyu Ai, Zhifen Chen, Chuan Shan, Huaiyuan Zheng, Juan Liu,

Xinyi Dai, Kai Li, Wei Wu, Jing Cao, Shuo Zhao. I can’t imagine six years’ life without you.

I am thankful for China Scholarship Council (CSC) and SFB824 for the support.

Finally the last, I would express my deepest appreciation to my parents, my wife Dr. Limin Yang

and my sister. Without your unconditional love, kind understanding and endless support, I can

barely finish this study. Thank you all!

Publications and Conferences

91


Papers:

Zhen Liu*, Ziying Jian*, Qian Wang, Tao Cheng, Benedikt Feuerecker, Markus

Schwaiger, Sung-Cheng Huang, Sibylle I. Ziegler and Kuangyu Shi. "A Continuously

Infused Microfluidic Radioassay System for the Characterization of Cellular

Pharmacokinetics." Accepted 08.02.2016 by Journal of Nuclear Medicine. (*: co-first

author).

Qian Wang, Zhen Liu, Sibylle I. Ziegler and Kuangyu Shi. "Enhancing Spatial

Resolution of 18F Positron Imaging with the Timepix Detector by Classification of

Primary Fired Pixels Using Support Vector Machine." Physics in Medicine and Biology

60, no. 13 (2015): 5261.

Qian Wang, Zhen Liu, Sibylle I. Ziegler and Kuangyu Shi. "A Reaction-Diffusion

Simulation Model of 18F-FDG Pet Imaging for the Quantitative Interpretation of Tumor

Glucose Metabolism." In Computational Methods for Molecular Imaging, edited by Fei

Gao, Kuangyu Shi and Shuo Li, 22, 123-137: Springer International Publishing, 2015.

Xiaoyin Cheng, Zhoulei Li, Zhen Liu, Nassir Navab, Sung-Cheng Huang, Ulrich Keller,

Sibylle I. Ziegler and Kuangyu Shi. "Direct Parametric Image Reconstruction in

Reduced Parameter Space for Rapid Multi-Tracer PET Imaging." IEEE Transactions on

Medical Imaging 34, no. 7 (2015): 1498-1512.

Qian Wang, Jan Tous, Zhen Liu, Sibylle I. Ziegler and Kuangyu Shi. "Evaluation of

Timepix Silicon Detector for the Detection of 18f Positrons." Journal of Instrumentation

9, no. 05 (2014): C05067.

Conference talks

Zhen Liu, Benedikt Feuerecker, Stephan Düwel, Tao Cheng, Geoffrey Topping, Katja

Steiger, Rickmer Braren, Markus Schwaiger, Sibylle Ziegler and Kuangyu Shi. "A

Multimodal Intravital Molecular Imaging System Based on Dorsal Skin Window

Chamber Tumor Model." Journal of Nuclear Medicine 56, no. supplement 3 (2015): 59.

Zhen Liu, Kuangyu Shi, Wei Sha, Sabine Pirsig, Sung-Cheng Huang, Markus

Schwaiger and Sibylle Ziegler. "Factors Influencing Cellular 18F-FDG Kinetics of

Tumor Cell Lines as Assessed by a Real-Time Radioassay System." Journal of Nuclear

Medicine 53, no. supplement 1 (2012): 9.

Patent:


92

Kuangyu Shi, Zhen Liu, Sibylle I. Ziegler and Markus Schwaiger. "System and

Apparatus for Multimodality-Compatible High-Quality Intravital Radionuclide

Imaging." US 20140121493 A1, 2014.

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